Soft Computing and Human-Centered Machines

Computer Science Workbench is a monograph series which will provide you with an in-depth working knowledge of current developments in computer technology. Every volume in this series will deal with a topic of importance in computer science and elaborate on how you yourself can build systems related to the main theme. You will be able to develop a variety of systems, including computer software tools, computer graphics, computer animation, database management systems, and computer-aided design and manufacturing systems. Computer Science Work­ bench represents an important new contribution in the field of practical computer technology. Tosiyasu L. Kunii Preface With the advent of digital computers some five decades ago and the wide­ spread use of computer networks recently, we have gained enormous power in gathering information and manufacturing. Yet, this increase in comput­ ing power has not given us freedom in a real sense, we are increasingly enslaved by the very machine we built for gaining freedom and efficiency. Making machines to serve mankind is an essential issue we are facing. Building human-centered systems is an imperative task for scientists and engineers in the new millennium. The topic of human-centered servant modules covers a vast area. In our projects we have focused our efforts on developing theories and techn!ques based on fuzzy theories. Chapters 2 to 12 in this book collectively deal with the theoretical, methodological, and applicational aspects of human­ centered systems. Each chapter presents the most recent research results by the authors on a particular topic.

1 Introduction.- 1.1 The Third Industrial Revolution: human-centered machines.- 1.2 Soft Computing: a unifying framework for intelligent systems.- 2 Multisets and Fuzzy Multisets.- 2.1 Introduction.- 2.2 Multisets.- 2.3 Fuzzy Multisets.- 2.3.1 Basic Operations of Fuzzy Multisets.- 2.4 Infinite Fuzzy Multisets.- 2.4.1 Infinite Sequence of Memberships and Computability.- 2.4.2 Operations for Infinite Fuzzy Multisets.- 2.5 Another Ftizzification.- 2.6 Application to Query Language for Fuzzy Database.- 2.6.1 Fuzzy Multirelations.- 2.6.2 Functions in Fuzzy SQL.- 2.7 Conclusion.- 2.8 References.- 3 Modal Logic, Rough Sets, and Fuzzy Sets.- 3.1 Introduction.- 3.2 Language for Modal Logic.- 3.3 Kripke Semantics for Modal Logic.- 3.4 Truth Sets and Generalized Lower and Upper Approximations.- 3.5 Validity.- 3.6 What is a System of Modal Logic?.- 3.7 Normal Systems of Modal Logic.- 3.8 Soundness.- 3.9 Completeness.- 3.10 Fuzzy Sets and Rough Sets.- 3.11 Concluding Remarks.- 3.12 References.- 4 Fuzzy Cognitive Maps: Analysis and Extensions.- 4.1 Introduction.- 4.2 Fuzzy Cognitive Maps.- 4.2.1 Causality and Logical Implication.- 4.2.2 Building Fuzzy Cognitive Maps.- 4.2.3 Causal Inference in FCM.- 4.2.4 Combining Fuzzy Cognitive Maps.- 4.3 Extensions to FCM.- 4.3.1 FCM with Non-linear Edge Functions.- 4.3.2 FCM with Constant Time-Delays.- 4.3.3 Weighted Combination of FCMs.- 4.4 Analysis of Fuzzy Cognitive Maps.- 4.4.1 FCM and Its State Space.- 4.4.2 Causal Module of FCM.- 4.4.3 Inference Patterns of Basic FCMs.- 4.4.4 Inference Pattern of General FCMs.- 4.5 Conclusions.- 4.6 References.- 5 Methods in Hard and Fuzzy Clustering.- 5.1 Introduction.- 5.2 Basic Methods in Clustering.- 5.3 Fuzzy c-Means.- 5.4 Other Nonhierarchical Methods.- 5.5 A Numerical Example.- 5.6 Fuzzy Hierarchical Clustering.- 5.7 Conclusions.- 5.8 References.- 6 Soft-Competitive Learning Paradigms.- 6.1 Introduction.- 6.2 Learning by Neural Networks.- 6.2.1 Supervised Learning.- 6.2.2 Unsupervised Learning.- 6.2.3 Reinforcement Learning.- 6.3 Competitive Learning Paradigm.- 6.3.1 Classic Competitive Learning.- 6.4 Overview of Competitive Learning Schemes.- 6.4.1 Winner-Take-Most (WTM) Paradigm.- 6.4.2 Competitive Learning with Conscience.- 6.4.3 Penalizing in Competitive Learning.- 6.4.4 Learning Schemes with Variable Number of Prototypes.- 6.4.5 Fuzzy Clustering Algorithms.- 6.5 Fuzzy Competitive Learning and Soft Competition.- 6.5.1 Conscience and Frequency Sensitive Competitive Learning.- 6.5.2 Rival Penalized Competitive Learning.- 6.6 Compensated Competitive Learning.- 6.6.1 The Concept of Compensated Competitive Learning.- 6.6.2 Varying the Number of Penalized Vectors in CCL.- 6.7 Conclusions.- 6.8 References.- 7 Aggregation Operations for Fusing Fuzzy Information.- 7.1 Introduction.- 7.2 Intersection and Union of Fuzzy Sets.- 7.3 Weighted Unions and Intersections.- 7.4 Uninorms.- 7.5 Mean Aggregation Operators.- 7.6 Ordered Weighted Averaging Operators.- 7.7 Linguistic Quantifiers and OWA Operators.- 7.8 Aggregation Using Fuzzy Measures.- 7.9 Conclusion.- 7.10 References.- 8 Fuzzy Gated Neural Networks in Pattern Recognition.- 8.1 Introduction.- 8.2 Generalized Gated Neuron Model.- 8.3 Fuzzy Gated Neural Networks.- 8.3.1 System Structure.- 8.3.2 Input, Gate, and Output Functions.- 8.3.3 Temporal Aggregation.- 8.4 Comparison between FGNN and STFM.- 8.4.1 FGNN’s Operational Characteristics.- 8.5 Experimental Results.- 8.5.1 2D Real World Texture Data.- 8.5.2 3D Synthetic Images.- 8.5.3 Real Range Images.- 8.5.4 Results and Discussions.- 8.6 Improvements to FGNN.- 8.6.1 Performance under Noisy Data.- 8.6.2 Noise Cover in FGNN.- 8.6.3 Knowledge Acquisition and Aggregation.- 8.7 The Improved FGNN.- 8.7.1 Mean and Bayesian Aggregation Methods.- 8.7.2 Alternative Aggregation Methods.- 8.8 Conclusions.- 8.9 References.- 9 Soft Computing Technique in Kansei (Emotional) Information Processing.- 9.1 Introduction.- 9.2 Concept of Kansei Information.- 9.2.1 Difference Between Intelligent Information and Kansei Information.- 9.2.2 Kansei Information from Soft Computing Approaches.- 9.2.3 Facial Expressions as Kansei Information.- 9.3 Study Examples of Facial Expressions.- 9.3.1 Recognition Model of Emotions Through Facial Expressions Considering Situations.- 9.3.2 Application of Recognition Model of Emotions Through Facial Expressions Considering Situations.- 9.3.3 Facial Caricature Drawing.- 9.4 Conclusions.- 9.5 References.- 10 Vagueness in Human Judgment and Decision Making.- 10.1 Introduction.- 10.2 Theoretical Representation of Vagueness in Judgment and Decision Making.- 10.3 Measurement and Fuzzy-Set Representation of Vagueness in Judgment and Decision Making.- 10.4 Experimental Studies of Vagueness of Judgment and Decision Making Using the Fuzzy Rating Method.- 10.5 Regression Analyses for Fuzzy Rating Data.- 10.6 Conclusion.- 10.7 References.- 11 Chaos and Time Series Analysis.- 11.1 Introduction.- 11.2 Embedding Time Series Data.- 11.3 Deterministic Nonlinear Prediction.- 11.4 Analysis of Complicated Time Series by Deterministic Nonlinear Prediction.- 11.5 Engineering Applications of Deterministic Nonlinear Prediction.- 11.6 Chaotic Time Series Analysis and Statistical Hypothesis Testing.- 11.7 Conclusions.- 11.8 References.- 12 A Short Course for Fuzzy Set Theory.- 12.1 Classical Sets.- 12.2 Fuzzy Sets.- 12.3 Basic Operations on Fuzzy Sets.- 12.4 Extension Principle.- 12.5 Fuzzy Relations.- 12.6 Possibility and Necessity Measures.- 12.7 Fuzzy Numbers.- 12.8 Discussion and Remarks.- 12.9 References.