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.

Most recent research and development results in human centered systems in the soft computing framework

Klappentext

Today's networked world and the decentralization that the Web enables and symbolizes have created new phenomena: information explosion and saturation. To deal with information overload, our computers should have human-centered functionality and enhanced intelligence, but instead they simply become faster. Soft computing is a unifying framework that combines techniques in neural networks, fuzzy theory, genetic algorithms, and artificial intelligence to develop intelligent systems able to learn in dynamic, imprecise, and uncertain environments. This book explains the theory, methodology, and application aspects of human-centered systems, showing how it is possible to extend to machines such techniques as dynamic cognitive learning, neural-fuzzy-based learning, and genetic-evolutionary type learning paradigms.

Inhalt
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.- 2.4 Infinite Fuzzy Multisets.- 2.5 Another Ftizzification.- 2.6 Application to Query Language for Fuzzy Database.- 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.- 4.3 Extensions to FCM.- 4.4 Analysis of Fuzzy Cognitive Maps.- 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.- 6.3 Competitive Learning Paradigm.- 6.4 Overview of Competitive Learning Schemes.- 6.5 Fuzzy Competitive Learning and Soft Competition.- 6.6 Compensated Competitive Learning.- 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.- 8.4 Comparison between FGNN and STFM.- 8.5 Experimental Results.- 8.6 Improvements to FGNN.- 8.7 The Improved FGNN.- 8.8 Conclusions.- 8.9 References.- 9 Soft Computing Technique in Kansei (Emotional) Information Processing.- 9.1 Introduction.- 9.2 Concept of Kansei Information.- 9.3 Study Examples of Facial Expressions.- 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.