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Windows 2000 Quick Fixes targets a selection of common problems, first- time tasks, and infrequently used features and provides quick and (usually) simple solutions to those issues. New users and users who are experienced but not familiar with the Windows 2000 interface will find tips on customiz- ing a wide range of operating system parameters that control everything from the way the desktop looks and functions to how the operating system does its job behind the scenes. Users of all levels will find quick and concise instructions that address problems and features in many areas of Windows 2000, including hardware and software configuration, security, networking, remote access, and a range of other features and functions.

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When using GUI-based software, we often focus so much on the interface that we forget about the general concepts required to use the software effectively. Access Database Design & Programming takes you behind the details of the interface, focusing on the general knowledge necessary for Access power users or developers to create effective database applications. The main sections of this book include: database design, queries, and programming.

Foreword Dedication Chapter 1—Fuzzy set-based modelling and simulation environment 1.1 Introduction: a rationale 1.2 System modelling with fuzzy sets 1.2.1 The paradigm 1.2.2 The general architecture of fuzzy models and the methodology of their development 1.3 References Chapter 2—Development of input interfaces 2.1 The frame of cognition 2.1.1 Definition 2.1.2 Properties 2.2 Uncertainty representation in the frame of cognition 2.2.1 Possibility and necessity measures 2.2.2 Taxonomy of uncertainty: conflict and ignorance 2.2.3 Uncertainty representation — activation planes 2.3 Reconstruction criterion 2.4 The criterion of entropy equalization 2.5 References Chapter 3—Fuzzy neural networks: models and learning 3.1 From neural networks and fuzzy sets to fuzzy neurocomputations 3.2 Logic-based neurons 3.2.1 Aggregative OR and AND logic neurons 3.2.2 OR/AND neurons 3.2.3 Logic neurons and an OWA aggregation operator 3.2.4 Computational enhancements of fuzzy neurons 3.2.5 Logic neurons with feedback 3.2.6 Referential logic-based neurons 3.2.7 Fuzzy threshold neuron 3.3 Classes of fuzzy neural networks 3.3.1 Approximation of logical relationships — development of the logic processor 3.3.2 Referential processor 3.4 Learning 3.4.1 Learning a single neuron 3.4.2 General policies for the parametric learning — reductions and expansions 3.5 Genetic Algorithms in structural learning of fuzzy neural networks 3.5.1 Prerequisites — Genetic Algorithms as a tool for global optimization 3.5.2 Hybridization: Gradient-based and genetic-oriented schemes in learning fuzzy neural networks 3.5.3 The stratified GA learning in fuzzy neural networks 3.6 Selected aspects of knowledge representation in fuzzy neural networks 3.6.1 Representing and processing uncertainty 3.6.2 Induced Boolean and core neural networks 3.7 Conclusions 3.8 References Chapter 4—Fuzzy neurocomputations 4.1 Decomposition problem 4.2 Implementation of the Tchebyschev and Hausdorff distances 4.3 Optimal vector quantization 4.4 Neural models of fuzzy decision-making 4.5 Rule induction 4.6 Fuzzy Computational Memories (FCM) 4.6.1 The architecture and its functional modules 4.6.2 Learning 4.7 Implicitly-supervised fuzzy pattern recognition 4.7.1 Problem formulation 4.7.2 The General Architecture 4.7.3 The Design of the Classifier 4.8 Neural network realization of a pseudomedian filter 4.9 Ranking fuzzy sets defined in R 4.10 Conclusions 4.11 References Chapter 5—Development of output interfaces 5.1 Linguistic to numerical mapping 5.1.1 Reconstruction criterion 5.1.2 Triangular fuzzy sets in 5.2 Transformation of nonnumerical inputs 5.3 Fuzzy set reconstruction 5.4 Linguistic interpretation 5.5 Optimization of fuzzy models 5.5.1 Validation of fuzzy models 5.5.2 Hierarchy of memories in fuzzy models and learning policies 5.6 Concluding comments 5.7 References Chapter 6—Fuzzy controller 6.1 The basic architecture 6.2 Fuzzy Hebbian learning 6.3 Compilation and interpretation of fuzzy controllers 6.4 Input and output interfaces of the fuzzy controller 6.4.1 Realization of the output interface 6.4.2 Robustness of the fuzzy controller 6.4.3 Nonnumerical input information 6.5 Validation of the fuzzy controller 6.5.1 Static validation 6.5.1.1 Completeness and structural fault-tolerance 6.5.1.2 Conflict 6.5.2 Dynamic modifications 6.5.2.1 Modifications of the input interface of the controller 6.5.2.2 Rule modification 6.6 Extensions of the fuzzy controller 6.6.1 Fuzzy relational equations as a development framework of fuzzy controllers 6.6.2 Fuzzy logic controller 6.7 Hybrid control structures 6.7.1 Switching between fuzzy controller and PID controller 6.7.2 Supervisory control 6.8 Concluding comments 6.9 References Chapter 7—Software development tools in designing fuzzy systems 7.1 The general development framework of fuzzy inference schemes 7.2 Classes of software resources 7.3 Hardware versus software implementation 7.3.1 High-speed fuzzy controllers: a genuine need or (in)expensive extravagance? 7.4 Selected software development tools 7.4.1 MANIFOLD EDITOR and MANIFOLD GRAPHICS EDITOR 7.4.2 FUZZY LOGIC DESIGNER ver. 1.0 7.4.3 FuzzyTECH 3.0 Explorer Edition 7.4.4 Linguistic Fuzzy Logic Controller for Education LFLC-edu ver. 1.0 7.4.5 Fuzzy Logic Development Kit (FULDEK) 7.4.6 MATRIXx/SystemBuild 7.4.7 A Fuzzy Logic Knowledge base generator for the MC68HC11 and MCH68HC05 Inference Engines 7.4.8 Fuzz-C, a preprocessor for fuzzy logic, ver 1.00 7.4.9 FuziCalc ver. 1.00 for Microsoft Windows 7.5 Designing fuzzy controllers with the use of simulation packages 7.6 Conclusions Chapter 8—Fuzzy Control 8.1 Defining notions of fuzzy control 8.1.1 Notions of a single step and multistep constraint-free control 8.2 Stability 8.3 Controllability with constraint requirements 8.4 Fuzzy controller as a knowledge-based control paradigm 8.4.1 Trial-and-error design policies 8.4.2 The architecture of the fuzzy controller 8.5 Control in fuzzy models 8.5.1 Problem formulation 8.5.2 The architecture 8.5.3 Defining objectives of fuzzy control 8.6 Control determination 8.6.1 On-line computations 8.6.2 Off-line control 8.7 Conclusions 8.8 References Chapter 9—Fuzzy flip-flops in information processing 9.1 From JK flip-flops to fuzzy flip-flops 9.1.1 Two-valued JK flip-flop 9.1.2 Design 9.1.3 Generalized fuzzy flip-flop 9.1.4 Fuzzy flip-flop 9.2 Fuzzy flip-flop and its neural network realization 9.3 System design through learning 9.4 Boolean and core structures of fuzzy flip-flops 9.5 Information processing with fuzzy flip-flops 9.5.1 Distributed modelling and its flip-flop realization 9.5.2 Realization of fuzzy algorithmic state machines 9.5.3 Memory-enhanced fuzzy controller 9.5.4 Dynamical pattern classifier 9.6 Conclusions 9.7 References Chapter 10—Fuzzy petri nets 10.1 Introduction 10.2 Petri nets and their fuzzy set-based extensions 10.3 Fuzzy Petri nets 10.3.1 Models of transitions and places 10.3.2 Boolean analysis of the net 10.4 The neural network model of the fuzzy Petri net and its enhancements 10.4.1 Inhibition mechanism in fuzzy Petri nets and its representation 10.4.2 Modelling input and output places — more detailed neural models 10.5 Examples 10.6 Representing rules in fuzzy Petri nets 10.7 Fuzzy controller realized as a fuzzy Petri net 10.8 Learning 10.9 Inverse problem in fuzzy Petri net 10.10 Conclusion 10.11 References Appendix A Appendix B Appendix C Index

Preface Chapter 1—Introduction 1.1 Neuroinformatics 1.1.1 Neural Memory: Neural Information Storage 1.1.2 Information-Traffic in the Neurocybernetic System 1.2 Information-Theoretic Framework of Neurocybernetics 1.3 Entropy, Thermodynamics and Information Theory 1.4 Information-Theoretics and Neural Network Training 1.4.1 Cross-Entropy Based Error-Measures 1.4.2 Symmetry Aspects of Divergence Function 1.4.3 Csiszár’s Generalized Error-Measures 1.4.4 Jaynes’ Rationale of Maximum Entropy 1.4.5 Mutual Information Maximization 1.5 Dynamics of Neural Learning in the Information-Theoretic Plane 1.6 Neural Nonlinear Activity in the Information-Theoretic Plane 1.7 Degree of Neural Complexity and Maximum Entropy 1.8 Concluding Remarks Bibliography Appendix 1.1 Concepts and Definitions in Information Theory Appendix 1.2 Functional Equations Related to Information Theory Appendix 1.3 A Note on Generalized Information Functions Chapter 2—Neural Complex: A Nonlinear C3I System? 2.1 Introduction 2.2 Neural Information Processing: CI Protocols 2.3 Nonlinear Neuron Activity 2.4 Bernoulli-Riccati Equations 2.5 Nonlinear Neural Activity: Practical Considerations 2.5.1 Stochastical Response of Neurons Under Activation 2.5.2 Representation of a Neuron as an Input-Dictated Cybernetic Regulator with a Quadratic Cost-function 2.5.3 Generalized Information-Theoretic Entropy Measure 2.5.4 Influence of Nonlinear Neuronal Activity on Information 2.5.5 Significance of the Parameter Q in the Generalized Bernoulli Function LQ(.) Depicting the Neuronal Input-output Relations 2.6 Entropy / Information Flow across a Neural Nonlinear Process 2.7 Nonsigmoidal Acti

Preface About the Editors Part 1—Fundamentals and Neuro-Fuzzy Signal Processing Chapter 1—Fuzzy Logic and Neuro-Fuzzy Systems in Medicine and Bio-Medical Engineering: A Historical Perspective 1. The First Period: The Infancy 2. Further Developments and Background 3. Neuro-Fuzzy Systems and their Applications in Medicine and Biology 4. Genetic Algorithms, Fuzzy Logic, and Neuro-Fuzzy Systems 5. Bibliographies 6. Conclusions and Predictions Chapter 2—The Brain as a Fuzzy Machine: A Modeling Problem 1. The Fuzzy Approach in Neurobiology: A Historical Perspective 2. The Generality of Young’s Hypothesis 2.1 Simple Stimuli 2.2 Neural Organization of Cryptic Events: From Tastes to Faces 2.2.1 The General Approach 2.2.2 Solutions Possible: Taste 2.2.3 Solutions Possible: Faces 2.3 Neural Codes 3. Fuzzy Models for Taste 3.1 Grades of Membership in Fuzzy Sets 3.2 A Fuzzy Model 3.2.1 The Model 3.2.2 The Synthesis of the Fuzzy Model 3.2.3 Simulating the Dynamics of Taste Neurons 4. Fuzzy Model For Brain Activity 4.1 A Neural Network Implementing a Fuzzy Machine? 4.2 An Artificial Neuron Implements a Fuzzy Membership Function 4.3 A Layer of Neurons Implements a Fuzzifier 4.4 A “Hidden” Neuron Implements a Fuzzy Rule 5. Applications of Fuzzy Logic to Neural Systems 5.1 Quantitative Aspects of the Fuzzy Neural Sets 5.1.1 Neural Mass 5.1.2 Sensitivity to Fine Gradations in Input 5.1.3 Intelligence 5.2 Defuzzification and Responses 5.3 Memory: Input and Retrieval 6. Conclusions Appendix 1. Abbreviations Appendix 2. Terminology References Chapter 3—Brain State Identification and Forecasting of Acute Pathology Using Unsupervised Fuzzy Clustering of EEG Temporal Patterns 1. Introduction 2. Background 2.1 The Electroencephalogram (EEG) Signal [1], [2] 2.2 Brain States and the EEG 2.3 Stimulus-Evoked EEG Patterns 2.4 Underlying Processes 2.5 Fuzzy Systems and the EEG 3. Tools 3.1 Data Acquisition 3.1.1 Spontaneous Ongoing Signal 3.1.2 Evoked Responses 3.2 Feature Extraction 3.2.1 Spectrum Estimation 3.2.2 Time-Frequency Analysis 3.2.2.1 Multiscale Decomposition By The Fast Wavelet Transform 3.2.2.2 Multichannel Model-Based Decomposition by Matching Pursuit 3.3 The Unsupervised Optimal Fuzzy Clustering (UOFC) Algorithm. 3.4 The Weighted Fuzzy K-Mean (WFKM) Algorithm 3.5 The Clustering Validity Criteria 4. Examples of Uses 4.1 Sleep-Stage Scoring 4.2 Forecasting Epilepsy 4.3 Classifying Evoked and Event-Related Potentials by Waveform 5. Concluding Remarks and Future Applications 5.1 Dynamic Version of State Identification by UOFC 5.2 Data Fusion Appendiex 1: The Fast Wavelet Transform Appendix 2: Multichannel Model-Based Decomposition by Matching Pursuit Appendix 3: Feature Extraction and Reduction by Principal Component Analysis List of Acronyms References Chapter 4—Contouring Blood Pool Myocardial Gated SPECT Images with a Sequence of Three Techniques Based on Wavelets, Neural Networks, and Fuzzy Logic 1. Introduction 2. Anatomy of the G-SPECT Images 3. Strategy of the Proposed Method 3.1. Overview of the Method 3.2. Wavelets-Based Image Pre-Processing 3.3. Neural Network Based Image Segmentation 3.4. Fuzzy Logic-Based Recognition of the Regions of Interest (Ventricles) 3.4.1. Definition of the Required Fuzzy Sentences 3.4.2. Combining Neuronal Approaches and Fuzzy Logic-Based Inference Systems 3.5. Training the Recognition System Using a Neuro-Fuzzy Technique 3.5.1. Automated Generation of Rules and Membership Functions (ALGORAM) 3.5.2. Adjustment of Membership Functions Using a Descent Method (FUNNY) 3.5.3. Combining the Automated Generation of Rules and Membership Functions and the Adjustment of their Parameters in a Parallel Implementation (FUNNY-ALGORAM) 4. In Vitro Experiments and Application to Medical Cases 4.1. Experiments with Phantoms 4.2. Clinical Test Cases 4.3. Implementation Issues 5. Conclusions References Chapter 5—Unsupervised Brain Tumor Segmentation Using Knowledge-Based Fuzzy Techniques 1. Introduction 2. Domain Background 2.1 Slices of Interest for the Study 2.2 Basic MR Contrast Principles 2.3 Knowledge-Based Systems 2.4 System Overview 3. Classification Stages 3.1 Stage Zero: Pathology Detection 3.2 Stage One: Building the Intra-Cranial Mask 3.3 Stage Two: Multi-spectral Histogram Thresholding 3.4 Stage Three: “Density Screening” in Feature Space 3.5 Stage Four: Region Analysis and Labeling 3.5.1 Removing Meningial Regions 3.5.2 Removing Non-Tumor Regions 3.6 Stage Five: Final T1 Threshold 4. Results 4.1 Knowledge-Based vs. Supervised Methods 4.2 Evaluation Over Repeat Scans 5. Discussion References Abbreviations Part 2—Neuro-Fuzzy Knowledge Processing Chapter 6—An Identification of Handling Uncertainties Within Medical Screening: A Case Study Within Screening for Breast Cancer 1. Introduction 2. Screening 2.1 Notations 2.2 The Screening Program 2.3 The Methods 3. The Select Function 3.1 The Decision Step 3.2 Disease-Specific Knowledge 3.3 The Refinement Step 4. A Breast Cancer Case Study 4.1 Minimizing A0 as Much as Possible in One Step 4.2 Finding the Screening Method 4.3 Defining Disease-Specific Knowledge 4.4 Performing the Refinement 4.5 The Integrated System 5. Conclusions and Further Work References Chapter 7—A Fuzzy System For Dental Developmental Age Evaluation 1. Introduction 2. Technical Consideration 2.1 Basic Conception of the Teeth Evaluation System 2.2 Rule Evaluation Module 3. System Optimization by Using Clinical Data 3.1 Material and Method 3.2 Dimensionality Analysis by Principal Component Analysis 3.3 System Optimization by Using Genetic Algorithm 3.4 System Evaluation and Results 4. Discussion and Conclusions Chapter 8—Fuzzy Expert System For Myocardial Ischemia Diagnosis 1. Introduction 2. Fuzzy Expert Systems 3. Difus - Hierarchical Diagnosis Fuzzy System 3.1 Characteristics 3.2 Knowledge Organization 3.3 Structure 3.4 Operation 4. Multimethod Myocardial Ischemia Diagnosis 5. Multimethod Myocardial Ischemia Diagnosis System 5.1 The Implementation of Fuzzy Score-Based Tests 5.1.1 Medical Patterns 5.1.2 Sequential Processing 5.1.3 Compact Representation of Fuzzy Score-Based Tests 5.2 MMIDS Structure and Operation 5.2.1 MMIDS Secondary Group 5.2.2 MMIDS Primary Groups 5.3 Experimental Results 6. Conclusions References Chapter 9—Design and Tuning of Fuzzy Rule-Based Systems for Medical Diagnosis 1. Introduction 2. Problem Statement and General Methodology 3. Design and Rough Tuning of Fuzzy Rules 3.1. Matrix of Knowledge 3.2. Fuzzy Model with Discrete Output 3.3. Fuzzy Model with Continuous Output 3.4. Rough Tuning of Fuzzy Rules 3.4.1. Rough Tuning of Membership Functions 3.4.2. Rough Tuning of Rules Weights 4. Fine Tuning of the Fuzzy Rules with Continuous Output 4.1. Tuning as a Problem of Optimization 4.2. Quality Evaluation of Fuzzy Inference 4.3. Computer Simulation 4.3.1. Experiment 1 4.3.2. Experiment 2 5. Fine Tuning of the Fuzzy Rules with Discrete Output 5.1. Tuning as a Problem of Optimization 5.2. Quality Evaluation of Fuzzy Inference 5.3. Computer Simulation 6. Application to Differential Diagnosis of Ischemia Heart Disease 6.1. Diagnosis Types and Parameters of Patient’s State 6.2. Fuzzy Rules 6.3. Fuzzy Logic Equation 6.4. Rough Membership Functions 6.5. Algorithm of Decision Making 6.6. Fine Tuning of The Fuzzy Rules in Medical Applications 7. Conclusions References Appendix 1—Comparison of real and inferred decisions for 65 patients Appendix 2—FUZZY EXPERT Shell and its Application Chapter 10—Integration of Medical Knowledge in an Expert System for Use in Intensive Care Medicine 1. Introduction 2. Software Design Principles 3. Medical Knowledge in Intensive Care Medicine 3.1. Structure of the Knowledge 3.2. Meaning of Colloquial Rules 3.3. Rule Processing and Result Calculation 3.4. Combining Different Rules 4. Transformation of Knowledge into FLORIDA Commands 4.1. Introduction 4.2. Comments 4.3. Modules 4.4. Linguistic Variables 4.5. The FLORIDA Calculator 4.6. Rules: The Knowledge Itself 4.7. Changing the Normal Value 5. Invocation of FLORIDA 6. Explaining More of FLORIDA’s Functionality — The Knowledge Base Inflammation 6.1. Structuring the Knowledge 6.2. Rules for Fever 6.3. Rules for Leukocytosis/Leukopenia 6.4. Rules for Tachycardia/Tachypnoe 6.5. Rules for Synthesis of Acute Phase Proteins 6.6. Rules for Consumption of Coagulation Components 6.7. Improvement of Explanation 7. Differentiation of Dysfunctions 8. Visualization of the Result 9. Discussion and Conclusions References Part 3—Neuro-Fuzzy Control and Hardware Chapter 11—Hemodynamic Management with Multiple Drugs using Fuzzy Logic 1. Introduction 1.1 Progress in Decision Making 1.2 Progress in Control 2. System Development 2.1 Decision-Making: Fuzzy Decision-Making Module (FDMM) 2.1.1 Purpose 2.1.2 Operation 2.2 Drug-Titration Control: Fuzzy Hemodynamic Control Module (FHCM) 2.2.1 Purpose 2.2.2 Operation 2.3 Supervisory Commands: Therapeutic Assessment Module (TAM) 2.4 System Evaluations 2.4.1 Example One 2.4.2 Example Two 3. Future Prospects 3.1 Design Possibilities 3.2 “Curse of Dimensions” 3.3 Machine Intelligence Additional Resources Appendix: Terminology References Chapter 12—Neuro-Fuzzy Hardware in Medical Applications 12 A.—System Requirements for Fuzzy and Neuro-Fuzzy Hardware in Medical Equipment 1. Introduction 2. Specific Requirements of Medical Applications 2.1 General System and Technological Requirements 2.2 Reliability Requirements 2.3 Precision and Sensitivity to Parameters 3. Analysis of Several Applications 3.1 Life-Support Applications 3.1.1 Artificial Heart Control 3.1.2 Assisted Ventilation 3.2 Anesthesia Related Equipment 3.3 Fuzzy and Neuro-Fuzzy-Based Equipment for Prosthetics 3.4 General Purpose Devices 3.5 Other Applications 4. General System Design Issues 4.1 Nonlinearity Implementation - Simulation Power 4.2 Dynamic Errors 5. Hardware Implementation Issues 5.1 Implementation Choice: Analog vs. Digital Fuzzy Processors 5.2 Hardware Minimization 5.3 Parallelism vs. Number of Rule Blocks 5.4 A Minimal System Design 6. Choosing the Right Design 7. Conclusions References Chapter 12 B—Neural Networks and Fuzzy-Based Integrated Circuit and System Solutions Applied to the Biomedical Field 1. Introduction 2. Required Properties for Embedded Medical Systems 2.1 Embedding medical systems 2.2 Autonomy 2.3 Reliability - safety 2.4 Precision of computation 2.5 Application-specific requirements 3. Architectures Applied to Neuro-Fuzzy IC Design 3.1 Artificial Neural Network Integrated Realization 3.2 Fuzzy-Based Integrated Realization 3.3 Hybrid Integrated Realization 3.4 An example of neuro-fuzzy realization 4. Concluding Remarks References Index of Terms

Preface CHAPTER 1—Introduction Merging Technologies Multimedia and Artificial Intelligence Important Definitions The Basics Multimedia Applications Text Sound Images Video Glue Tools Artificial Intelligence Rule-Based or Expert Systems Knowledge Representation Tools Other Books The Rest of the Book CHAPTER 2—Multimedia Authoring Tools Introduction Developing Your Own Multimedia Tool Versus Using a Commercially Available Tool Language-Based Versus Graphic Interface–Based Tools The Interaction Language The IL Language in Greater Detail IL’s Doorway Authorware Professional for Windows Recap The Importance of Paradigms Developing Multimedia Using Time Lines Visual Basic as a Multimedia Authoring Tool Introduction to Visual Basic The ATM Simulation in Visual Basic Some Comments about Visual Basic and the ATM Simulation Chapter Summary CHAPTER 3—The Multimedia Database Introduction Databases Knowledge as Media Architecture Records in the Multimedia Database An Example Viewers, Loaders, and Operations Retrieving Media by Content Retrieving Media by Characteristic Creating a Multimedia Database Chapter Summary Suggested Reading CHAPTER 4—Tools for Intelligent Applications Introduction Making a Thinking Machine The LISP Programming Language A Brief LISP Tutorial Logic as a Programming Language A Brief Prolog Tutorial Deriving Rules for the Operation of the Toaster Expert Systems and Expert System Languages What Is an Expert System? A Small-X Tutorial Rules to Make a Toaster Work Chapter Summary Suggested Reading CHAPTER 5—Knowledge Representation Introduction Many Approaches for Many Reasons Symbolic Representations Semantic Networks Introduction to Semantic Networks Using Semantic Networks Creating Your Own Semantic Networks Representing a Semantic Network A Tool for Creating Semantic Networks Logic Representations Introduction to Logic Representations Using Logic Representations Representation Structure Using Frames A Tool for Creating Frames Chapter Summary Suggested Reading CHAPTER 6—Models Introduction Introducing Models Why Models? Qualitative and Quantitative Models A Mini-How-To Guide Step 1: What are the Components in the Mechanism? Step 2: Identify Which Components Represent Sub-components of the Mechanism and Which Represent Connections between Mechanisms Step 3: Identify the Relationships between Components in the Model Step 4: Identify Implicit Relationships in the Mechanism Step 5: Specify the Rules of Operation of the Model Step 6: Test the model Model Examples PROUST A Process-Oriented Model Knowledge, Metaknowledge, Meta-metaknowledge A More Complete Toaster Step 1: What are the Components of the Mechanism? Step 2: Identify Which Components Represent Subcomponents of the Mechanism and Which Represent Connections between Mechanisms Steps 3 and 4: Identify Relationships between Components in the Model Step 5: Specify the Rules of Operation of the Model Step 6: Test the Model Chapter Summary Suggested Reading CHAPTER 7—A Toaster Tutor Introduction Just What Is an Intelligent Tutor? The Tutoring Domain Now That We Know What It Is, What Do We Want It to Do? Tutor Design Story Board How It Works Tutor Models Student Model Instructional Model Simulating the Simulation Chapter Summary Suggested Reading CHAPTER 8—Natural Language Processing and Intelligent Multimedia Introduction Multimedia and Natural Language Processing Natural Language Processing—The Details Propositions Are the Key Application Example 1—An Automatic Illustrator Elements of the Automatic Illustrator Application Example 2—A Foreign Language Tutor Named Habla Pedagogy A Proposed Description of Habla Chapter Summary Suggested Reading CHAPTER 9—The Future of Intelligent Multimedia What Does the Future Hold for Intelligent Multimedia? Advances in Artificial Intelligence Neural Nets Genetic Programming Advances in Multimedia Virtual Reality and Environments The Internet Suggested Reading APPENDIX A Index

Chapter 1—Preliminaries 1.1. Computational Intelligence: its inception and research agenda 1.2. Organization and readership 1.3. References Chapter 2—Neural Networks and Neurocomputing 2.1. Introduction 2.2. Generic models of computational neurons 2.3. Architectures of neural networks - a basic taxonomy 2.3.1. Radial Basis function neural networks 2.4. Learning in neural networks 2.4.1. Neural networks as universal approximators 2.4.2. Generic modes of learning in neural networks 2.4.3. Performance indexes in training of neural networks 2.5. Selected classes of learning methods 2.5.1. Gradient-based optimization of multivariable functions 2.5.2. Perceptron learning rule 2.5.3. Delta learning rule 2.5.4. Backpropagation learning 2.5.5. Hebbian learning 2.5.6. Competitive learning 2.5.7. Self-organizing maps 2.5.8. Learning in presence directly and indirectly labeled patterns 2.6. Generalization abilities of neural networks 2.7. Enhancements of gradient-based learning in neural networks 2.8. Concluding remarks 2.9. Problems 2.10. References Chapter 3—Fuzzy Sets 3.1. Introduction 3.2. Basic definition 3.3. Types of membership functions 3.4. Characteristics of a fuzzy set 3.5. Membership function determination 3.5.1. Horizontal method of membership estimation 3.5.2. Vertical method of membership estimation 3.5.3. Pairwise comparison method of membership function estimation 3.5.4. Problem specification-based membership determination 3.5.5. Membership estimation as a problem of parametric optimization 3.6. Fuzzy relations 3.7. Set theory operations and their properties 3.8. Triangular norms 3.9. Triangular norms as the models of operations on fuzzy sets 3.10. Information-based characteristics of fuzzy sets 3.10.1. Entropy measure of fuzziness 3.10.2. Energy measure of fuzziness 3.10.3. Specificity of a fuzzy set 3.11. Matching measures 3.11.1. Possibility and necessity measures 3.11.2. Compatibility measure 3.12. Numerical representation of fuzzy sets 3.13. Rough sets 3.14. Rough sets and fuzzy sets 3.15. Shadowed sets 3.16. The frame of cognition 3.16.1. Basic definition 3.16.2. Main properties 3.16.3. Approximation aspects of the frame of cognition 3.16.4. Robustness properties of the frame of cognition 3.17. Probability and fuzzy sets 3.18. Hybrid fuzzy-probabilistic models of uncertainty 3.19. Conclusions 3.20. Problems 3.21. References Chapter 4—Computations with Fuzzy Sets 4.1. Introductory remarks 4.2. The extension principle 4.3. Fuzzy numbers 4.3.1. Basic characteristics 4.3.2. Computing with fuzzy numbers 4.3.3. Accumulation of fuzziness in computing with fuzzy numbers 4.4. Fuzzy rule-based computing 4.4.1. Rules with fuzzy sets 4.4.2. A design of fuzzy rule - based systems 4.4.3. Fuzzy Hebbian learning and associative memory as a realization of rule-based systems 4.5. Fuzzy controller and fuzzy control 4.5.1. Generic concept of fuzzy control 4.5.2. Design principles of the fuzzy controller 4.5.3. Numerical experiments 4.5.4. Fuzzy scheduler 4.6. Rule-based systems with nonmonotonic operations 4.6.1. Nonmonotonic AND and OR operations: a generalization 4.6.2. Estimation problem of the default fuzzy set 4.6.3. Approximate reasoning with defaults 4.7. Conclusions 4.8. Problems 4.9. References Chapter 5—Evolutionary Computing 5.1. Introduction 5.2. Gradient-based and probabilistic optimization as examples of single-point search techniques 5.3. Genetic algorithms - fundamentals and a basic algorithm 5.4. Schemata Theorem - a conceptual backbone of GAs 5.5. From search space to GA search space 5.5.1. Gray coding 5.5.2. Floating point coding 5.6. Exploration and exploitation of the search space 5.7. Experimental studies 5.8. Classes of evolutionary computation 5.8.1. Evolutionary Strategies 5.8.2. Evolutionary Programming 5.8.3. Genetic Programming 5.9. Conclusions 5.10 Problems 5.11. References Chapter 6—Fuzzy Neural Systems 6.1. Introduction 6.2. Neurocomputing in fuzzy set technology 6.3. Fuzzy sets in the technology of neurocomputing 6.4. Fuzzy sets in the preprocessing and enhancements of training data 6.4.1. Nonlinear data normalization 6.4.2. Variable processing resolution - fuzzy receptive fields 6.5. Uncertainty representation in neural networks 6.6. Neural calibration of membership functions 6.6.1. The Optimization Algorithm 6.6.2. Neural network realization of the nonlinear mapping 6.7. Knowledge-based learning schemes 6.7.1. Metalearning and fuzzy sets 6.7.2. Fuzzy clustering in revealing relationships within data 6.7.2.1. Fuzzy perceptron 6.7.2.2. Conditional (context-sensitive) clustering as a preprocessing phase in neural networks 6.8. Linguistic interpretation of neural networks 6.8.1. From neural networks to rule-based systems 6.8.2. Linguistic Interpretation of self-organizing maps 6.9. Hybrid fuzzy neural computing structures 6.9.1. Architectures of hybrid fuzzy neural systems 6.9.2. Temporal aspects of interaction in fuzzy-neural systems 6.10. Conclusions 6.11. Problems 6.12. References Chapter 7—Fuzzy Neural Networks 7.1. Logic-based neurons 7.1.1. Aggregative OR and AND logic neurons 7.1.2. OR/AND neurons 7.1.3. Conceptual and computational augmentations of fuzzy neurons 7.1.3.1. Representing inhibitory information 7.1.3.2. Computational enhancements of the neurons 7.2. Logic neurons and fuzzy neural networks with feedback 7.3. Referential logic-based neurons 7.4. Learning in fuzzy neural networks 7.4.1. Learning policies for parametric learning in fuzzy neural networks 7.4.2. Performance index 7.4.3. Interpretation of fuzzy neural networks 7.5. Case studies 7.5.1. Logic filtering 7.5.2. Minimization of multiple output two-valued combinational systems 7.5.3. FNN as a model of approximate reasoning 7.5.4. Sensor fusion via fuzzy neurons 7.6. Conclusions 7.7. Problems 7.8. References Chapter 8—CI systems 8.1. Introduction 8.2. Fuzzy encoding in evolutionary computing 8.2.1. Direct methods of fuzzy encoding 8.2.2. Weak encoding with fuzzy sets 8.3. Fuzzy crossover operations 8.4. Fuzzy metarules in genetic computing 8.5. Relational structures and their optimization 8.5.1. Image compression as a problem of relation reduction 8.5.2. GA-optimized data mining 8.6. The Satisfiability Problem 8.7. Evolutionary rule-based modeling of analytical relationships 8.8. Genetic optimization of neural networks 8.8.1. Parametric optimization of neural networks 8.8.2. Fuzzy genetic optimization of neural networks 8.9. Genetic optimization of rule-based systems 8.10. Conclusions 8.11. Problems 8.12. References Index

Preface Acknowledgment Chapter 1—Introduction 1.1 Pattern Recognition Systems 1.2 Motivation For Artificial Neural Network Approach 1.3 A Prelude To Pattern Recognition 1.4 Statistical Pattern Recognition 1.5 Syntactic Pattern Recognition 1.6 The Character Recognition Problem 1.7 Organization Of Topics References And Bibliography Chapter 2—Neural Networks: An Overview 2.1 Motivation for Overviewing Biological Neural Networks 2.2 Background 2.3 Biological Neural Networks 2.4 Hierarchical Organization in the Brain 2.5 Historical Background 2.6 Artificial Neural Networks References and Bibliography Chapter 3—Preprocessing 3.1 General 3.2 Dealing with Input from a Scanned Image 3.3 Image Compression 3.3.1 Image Compression Example 3.4 Edge Detection 3.5 Skeletonizing 3.5.1 Thinning Example 3.6 Dealing with Input From a Tablet 3.7 Segmentation References and Bibliography Chapter 4—Feed-Forward Networks with Supervised Learning 4.1 Feed-Forward Multilayer Perceptron (FFMLP) Architecture 4.2 FFMLP in C++ 4.3 Training with Back Propagation 4.3.1 Back Propagation in C++ 4.4 A Primitive Example 4.5 Training Strategies and Avoiding Local Minima 4.6 Variations on Gradient Descent 4.6.1 Block Adaptive vs. Data Adaptive Gradient Descent 4.6.2 First-Order vs. Second-Order Gradient Descent 4.7 Topology 4.8 ACON vs. OCON 4.9 Overtraining and Generalization 4.10 Training Set Size and Network Size 4.11 Conjugate Gradient Method 4.12 ALOPEX References and Bibliography Chapter 5—Some Other Types of Neural Networks 5.1 General 5.2 Radial Basis Function Networks 5.2.1 Network Architecture 5.2.2 RBF Training 5.2.3 Applications of RBF Networks 5.3 Higher Order Neural Networks 5.3.1 Introduction 5.3.2 Architecture 5.3.3 Invariance to Geometric Transformations 5.3.4 An Example 5.3.5 Practical Applications References and Bibliography Chapter 6—Feature Extraction I: Geometric Features and Transformations 6.1 General 6.2 Geometric Features (Loops, Intersections, and Endpoints) 6.2.1 Intersections and Endpoints 6.2.2 Loops 6.3 Feature Maps 6.4 A Network Example Using Geometric Features 6.5 Feature Extraction Using Transformations 6.6 Fourier Descriptors 6.7 Gabor Transformations and Wavelets References And Bibliography Chapter 7—Feature Extraction II: Principal Component Analysis 7.1 Dimensionality Reduction 7.2 Principal Components 7.2.1 PCA Example 7.3 Karhunen-Loeve (K-L) Transformation 7.3.1 K-L Transformation Example 7.4 Principal Component Neural Networks 7.5 Applications References and Bibliography Chapter 8—Kohonen Networks and Learning Vector Quantization 8.1 General 8.2 The K-Means Algorithm 8.2.1 K-Means Example 8.3 An Introduction To The Kohonen Model 8.3.1 Kohonen Example 8.4 The Role Of Lateral Feedback 8.5 Kohonen Self-Organizing Feature Map 8.5.1 SOFM Example 8.6 Learning Vector Quantization 8.6.1 LVQ Example 8.7 Variations On LVQ 8.7.1 LVQ2 8.7.2 LVQ2.1 8.7.3 LVQ3 8.7.4 A Final Variation Of LVQ References And Bibliography Chapter 9—Neural Associative Memories and Hopfield Networks 9.1 General 9.2 Linear Associative Memory (LAM) 9.2.1 An Autoassociative LAM Example 9.3 Hopfield Networks 9.4 A Hopfield Example 9.5 Discussion 9.6 Bit Map Example 9.7 Bam Networks 9.8 A Bam Example References And Bibliography Chapter 10—Adaptive Resonance Theory (ART) 10.1 General 10.2 Discovering The Cluster Structure 10.3 Vector Quantization 10.3.1 VQ Example 1 10.3.2 VQ Example 2 10.3.3 VQ Example 3 10.4 Art Philosophy 10.5 The Stability-Plasticity Dilemma 10.6 ART1: Basic Operation 10.7 ART1: Algorithm 10.8 The Gain Control Mechanism 10.8.1 Gain Ccontrol Example 1 10.8.2 Gain Control Example 2 10.9 ART2 Model 10.10 Discussion 10.11 Applications References and Bibliography Chapter 11—Neocognitron 11.1 Introduction 11.2 Architecture 11.3 Example of a System with Sample Training Patterns References and Bibliography Chapter 12—Systems with Multiple Classifiers 12.1 General 12.2 A Framework for Combining Multiple Recognizers 12.3 Voting Schemes 12.4 The Confusion Matrix 12.5 Reliability 12.6 Some Empirical Approaches References and Bibliography Index

Preface Acknowledgments Chapter 1—Intelligent Control Techniques 1 Introduction 2 Knowledge-Based Systems 3 Neural Networks 3.1 Introduction 3.2 Biological Neuronal Morphology 3.3 Static Neural Networks 3.4 Common Types of Artificial Neural Networks 3.5 Backpropagation Learning Algorithm 4 Fuzzy Logic 4.1 Fuzzy Systems and Rules 4.2 Fuzzy Reasoning and Aggregation 4.3 Fuzzy Control 5 Evolutionary Computing 5.1 GA Searching Algorithm 5.2 GA Selection 5.3 GA Reproduction 6 Summary References Chapter 2—Learning and Adaptation in Complex Dynamic Systems 1 Introduction 2 Systems Identification and Adaptive Control 2.1 Techniques for Systems Identification 2.1.1 Nonparametric methods 2.1.2 Parametric methods 2.2 Adaptive Control 2.2.1 Gain scheduling 2.2.2 Model-referenced adaptive control 2.2.3 Self-tuning regulators 3 Learning Techniques 3.1 Symbolic Learning 3.2 Numerical Learning 4 Artificial Neural Networks 4.1 Multilayer Perceptron 4.2 Kohonen Self-Organizing Network 4.3 Neural Networks as Identification Tools 4.4 Other Applications of ANN 5 Summary References Chapter 3—Applications of Evolutionary Algorithms to Control and Design 1 Introduction 1.1 Some Basics 1.2 Types of Evolutionary Algorithms 1.3 Types of Operations 1.3.1 Representation of Chromosomes 1.3.2 Fitness Value 1.3.3 Selection 1.3.4 Recombination and Mutation 1.4 Applications 2 Applications in Control 2.1 Robot Control 2.1.1 Planning of Joint Configuration of Manipulators 2.1.2 Acquisition of Behavior Rules of Autonomous Mobile Robots 2.2 Communication System Control 3 Applications in Design 3.1 Circuit Design 3.2 Graphics Design 3.2.1 Interactive EA Approach 3.2.2 Fitness Estimation in Interactive Approach 3.3 Music Design 3.3.1 Sound Synthesis 3.3.2 Algorithmic Composition 4 Other Applications 5 Summary References Chapter 4—Neural Control Systems and Applications 1 Introduction 2 Artificial Neural Networks (ANN) 2.1 The Backpropagation Network (BPN) 2.2 Kohonen Networks 2.3 Counterpropagation Networks 2.4 Hebbian Networks 2.5 Radial Basis Function Networks 2.6 Hopfield Networks 3 Neural Modeling and Identification 4 Neurocontroller-Design Methods 4.1 Supervised Control 4.2 Direct Inverse Control 4.3 Neural Adaptive Control 4.4 Backpropagation Through Time 4.5 Reinforcement Learning Control 4.6 Hybrid Control 5 Hardware and Software for ANN 6 Modular Neural-Visual Servo Control System 6.1 Introduction 6.2 Modular Neural-Control System 6.2.1 Control Networks 6.2.2 Decision Networks 6.3 Evaluation System 6.4 Preliminary Results 6.5 Conclusion 7 Summary References Chapter 5—Feature Space Neural Filters and Controllers 1 Introduction 2 Feature Space Filtering Using Neural Networks 2.1 Adaptive Signal Processing for Pattern Recognition 2.2 General Structure of the FSF System 2.3 A FSF Architecture Using Adaptive Linear Combiner Filter and Radial Basis Function Network Feature Extractor 2.3.1 FSF Realization 2.3.2 ALC-RBF FSF System Learning 2.3.3 Image Contour Enhancement and Recognition Optimization Using ALC-RBF FSF System 2.3.4 Discussion 2.4 FSF Architecture Using Multilayer Perceptron Filter and Principal Component Analysis Network Feature Extractor 2.4.1 MLP-PCA FSF System Realization 2.4.2 Learning in MLP-PCA FSF System 2.4.3 Example of MLP-PCA Feature Space System in Signal Filtering 2.4.4 Discussion 2.5 Concluding Remarks 3 Feature Space Identification and Control Schemes Using Neural Networks 3.1 Feature Space Neural Identification Topologies 3.2 Feature Space Neural Control Topologies 3.2.1 Data Versus Feature Space Neural Control Topologies 3.2.2 Feature Space Control Example 3.2.3 Identification Neural Network 3.2.3.1 Feature Extractor 3.2.3.2 Control Network 3.3 Discussion and Concluding Remarks 4 Summary References Chapter 6—Discrete-Time Neural Network Control of Nonlinear Systems 1 Introduction 2 Background 2.1 Neural Networks 2.2 Advantage of NN Over Adaptive Controllers 2.3 Stability of Dynamical Systems 2.4 MIMO Dynamical Systems 2.5 Tracking Problem 3 Neural Network Controller Design 3.1 NN Controller Structure and Error System 3.2 Well-Defined Control Problem 3.3 Proposed Controller 3.4 Weight Updates for Guaranteed Performance 4 Passivity of Dynamical Systems 4.1 Passive Systems 4.2 Passivity of the Closed-loop System and NN 5 Simulation Results 6 Summary References Chapter 7—Robust Adaptive Control of Robots Based on Static Neural Networks 1 Introduction 2 Notation 2.1 Permutation Operator “⊗” 2.2 GL Product Operator “•” 3 Neural Network Approximation 4 Lagrange-Euler Formulation of Robots 5 Dynamic Modeling of Robots Using Neural Networks 6 Controller Design 7 Case Study 7.1 Trajectory Planning 7.2 Simulation Settings 7.3 Non-Adaptive Control 7.4 Adaptive Control 8 Summary References Chapter 8—Error Correction Using Fuzzy Logic in Vehicle Load Measurement 1 Introduction 2 Vehicle Load Indicator 3 Describing Vehicle Loading States 4 Fuzzy Reasoning 5 Simulation Experiment 6 Summary References Chapter 9—Intelligent Control of Air Conditioning Systems 1 Fuzzy Controlled Air Conditioning System for Energy Conservation Applied to the Synthetic Fiber Plant 1.1 Introduction 1.2 Role and Problems of Air Conditioning Equipment in Synthetic Fiber Plant 1.2.1 Role of Air Conditioners in Spinning and Drawing/Twisting Processes 1.2.2 Problems of Air Conditioners in Spinning and Drawing/Twisting Processes 1.3 Fuzzy-Controlled Air Conditioning System for Energy Conservation 1.4 Results 1.5 Future Directions 1.6 Acknowledgment 2 A Learning Type Fuzzy Logic Control for Stabilizing Temperature and Humidity in a Clean Room 2.1 Introduction 2.2 Ordinary Fuzzy Logic Control System 2.3 A Learning Type Fuzzy Logic Control System 2.3.1 Structure of a Hierarchical Fuzzy Model 2.3.2 Succession of the Ordinary Fuzzy Model 2.4 Simulation Experiments 2.5 Practical Results 2.6 Conclusion 3 Occupant Condition Detecting Algorithm for Air Conditioning Systems 3.1 Introduction 3.2 Structure of the Pyroelectric Infrared Rays Detector 3.3 Segmentation of Occupants from Thermal Images 3.3.1 Removing Background Using the Fuzzy C-Means Algorithm 3.3.2 Identifying the Number of Occupants 3.3.3 Region Growing Algorithm 3.4 Method to Locate Occupants 3.4.1 Estimating the Distance Between the Sensor and Occupants 3.4.2 Experimental Results 3.5 Conclusion References Chapter 10—Intelligent Automation Systems at Petroleum Plants in Transient State 1 PID Controller Using Neuro-Fuzzy Hierarchical System in Feed Oil Switching 1.1 Introduction 1.2 Process Description 1.3 Control Problems in Feed Oil Switching 1.4 Neuro-Fuzzy Hierarchical Control System 1.4.1 Prediction Function 1.4.2 Correction Function 1.5 Control Algorithm 1.6 Results 1.7 Conclusion 2 Fuzzy Control System in Pump Start-up 2.1 Introduction 2.2 Outline of Pump Start-up Operation 2.3 Problems to Automate by Conventional Controller 2.3.1 Ramp controller 2.3.2 PID controller 2.4 Fuzzy Controller 2.4.1 Input Variable and Output Variable 2.4.2 Fuzzy Control Rules 2.5 Results 2.6 Conclusion 3 Fuzzy-PID Hybrid Control System in Feed Property Changing 3.1 Introduction 3.2 Process Description 3.3 Control Problems 3.4 Fuzzy-PID Hybrid Control System 3.5 Parameter Tuning 3.6 Results 3.7 Conclusion References Chapter 11—Intelligent Control for Ultrasonic Motor Drive 1 Introduction 2 Ultrasonic Motor Drive 2.1 The Equivalent Model of the USM 2.2 The Driving Circuit for the USM 3 Fuzzy Model-Following Control 4 Neural Network Model-Following Control 4.1 Description of the Neural Network 4.2 On-Line Learning Algorithm 5 Fuzzy Neural Network Model-Following Control 5.1 Description of the Fuzzy Neural Network 5.2 On-Line Learning Algorithm 6 The PC-Based Ultrasonic Motor Drive 7 Experimental Results 7.1 Fuzzy Model-Following Control 7.2 Neural Network Model-Following Control 7.3 Fuzzy Neural Network Model-Following Control 8 Summary References Chapter 12—Intelligent Automation of Herring Roe Grading 1 Introduction 2 Herring Roe Grading Process 2.1 Pre-Extraction Stage 2.2 Main Grading 2.3 Price Negotiation 3 Grading Technology 3.1 Shape Analysis 3.2 Ultrasonic Echo Imaging for Firmness Measurement 3.3 Vision-Based Weight Estimation 3.4 Color Grading 3.5 Fuzzy Decision-Making System 4 Prototype Development 4.1 Conveyor System 4.2 Ejection Mechanism 4.3 Sensory System 4.4 Prototype Control System 5 Prototype Testing 5.1 Laboratory Experiments 5.2 On-Site Production Test 5.3 Performance Evaluation and Possible Improvements 6 Summary Acknowledgment References Chapter 13—Intelligent Techniques for Vehicle Driving Assistance 1 Introduction 2 Multisensor Data Fusion 2.1 Introduction 2.2 Sensors 2.2.1 Static Environment 2.2.2 Dynamic Environment 2.3 Temporal Data Fusion 2.3.1 The Static Environment Perception 2.3.2 The Dynamic Environment Perception 2.3.2.1 Definition of the sensor and global maps 2.3.2.2 The different steps of the filtering operation 2.3.2.3 Reliability definition 2.3.3 The Copilot Mapping 2.4 Conclusion 3 Vehicle Modeling for Supervision of Manoeuvres 3.1 Introduction 3.2 Supervision of Manoeuvres 3.3 Situation Analysis 3.3.1 The Dynamic Model of the Vehicle 3.3.2 Vehicle Following 3.3.3 Highway Access Manoeuvre 3.3.4 A Lane-Changing Manoeuvre 3.4 Manoeuvre Monitor 3.5 Danger Controller 3.6 Requests Generation and Sensors Planning 3.7 The Driver Information Level 3.8 Conclusion 4 On-Board Real-Time Expert System for Control of the Vehicle 4.1 Introduction 4.2 Development of an Expert System for Control 4.2.1 Building the Knowledge Base 4.2.2 Basic Functioning of the Expert System for Control 4.3 Development of the Real-Time Expert System 4.3.1 Integration of the Expert System in the Real-Time Environment 4.3.2 Asynchronous Data Flows 4.3.3 Control Strategies for the IP 4.3.4 Interrupt Handling 4.3.5 Temporal Reasoning and Multiagent KBS 4.4 Conclusion 5 Summary Notation References Chapter 14—Intelligent Techniques in Air Traffic Management 1 Introduction 1.1 Future Air Navigation Systems (FANS) 1.2 Air Traffic Management 1.3 ATM System Issues for FANS 1.4 Applying AI Technology to ATC 1.4.1 Scheduling and Planning 1.4.2 Agent Technology 2 Intelligent ATC Systems 2.1 OASIS 2.2 COMPAS 2.3 CTAS 3 Intelligent Air Traffic Flow Management 3.1 A Model of Air Traffic Flow Management 3.2 Scheduling for ATFM 3.3 Heuristics 4 The Air Traffic Simulation Test 4.1 Interactive Plan Steering Architecture 4.2 AirTFM - the ATFM Test 5 Real-Time Search Algorithm for Air Traffic Flow Management 5.1 Real-Time Search Algorithms 5.1.1 Real-Time Planning and Scheduling Algorithms 5.1.2 Real-Time Monitoring and Control Algorithms 5.2 Time-Dependent Heuristic Search (TDHS) 5.3 Complexity Analysis of TDHS 5.4 Time-Dependent Cost Function 5.5 Experimental Results on TDHS 5.5.1 The Traveling Salesperson Problem 5.5.2 Base Performance of Heuristics 5.5.3 Results of TDHS on TSP 6 Summary References Index Copyright © CRC Press LLC

Foreword Author's Biographical Information Part A—General Theory Chapter 1—Learning Algorithms for Neuro-Fuzzy Networks 1 Introduction 2 Neuro-Fuzzy Networks 2.1 The Conventional Fuzzy Model 2.2 From Fuzzy to Neuro-Fuzzy 2.3 Initialization 2.4 Training Procedure 3 Weight identification for Φ2 3.1 Gradient Descent 3.2 Least Square Method 3.3 Recursive Least Square Method 4 Optimisation of Membership Functions 4.1 First Order Approximation of Gradient 4.2 Random Optimization 5 Examples 5.1 Non Differentiable NFN 5.2 Differentiable NFN 6 Conclusion References Chapter 2—Towards a Unified Theory of Intelligent Autonomous Control Systems 1 Introduction 2 State of the Art and Future Challenges 2.1 Three views of what are the most important robotic issues 2.2 Some Pros and Cons of the Available Methodologies 3 Adaptive Systems and the Dual Control Problem 3.1 Identification vs. Optimization in Changing Environments 4 A Systemic Approach to Behavioural Specification of Intelligent Systems 4.1 A Conceptual Framework for Describing and Synthesizing Actions of an Intelligent System 5 Behavioural Specification of the Activities of a System of Interacting Actors 5.1 Models of Actors interacting with their Environments 6 Fuzzification of Formal Models of Adaptive Autonomous Control Systems 6.1 The Effects of Imprecision and Uncertainty 6.2 Fuzzification of Power Sets and Relational Systems 7 Fuzzy identification 8 Introducing Control Hierarchies

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