Studies in Fuzziness and Soft Computing

By: Vesa A Niskanen

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Hardcover | 19 August 2003

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292 Pages

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23.5 x 15.88 x 1.91

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This book considers Soft Computing methods and their applications in the human sciences. Soft Computing methods - including fuzzy systems, neural networks, evolutionary computing and probabilistic reasoning - are state-of-the-art methods in theory formation and model construction. They mainly stem from the natural sciences, and they have already proved to be powerful in their applications because Soft Computing models, particularly fuzzy system models, are simple and correspond well to the actual world and to human reasoning. Hence, we no longer have to use the complicated mathematical models that have prevailed in this research area. Dozens of books and thousands of articles have been devoted to applications of Soft Computing in the natural sciences, but only a few studies have focused on its applications in the human sciences, such as the social and the behavioral sciences - this despite the fact that these novel methods seem to open a number of inspiring prospects in these disciplines. In quantitative research in the human sciences, typical application areas include statistical models that can be replaced by simpler numerical or linguistic Soft Computing models. In qualitative research, Soft Computing methods can enhance modelling because, instead of having to do manual work, we can use computer simulations with approximate and/or linguistic constituents.

Industry Reviews

From the reviews:

"Dr. Niskanens work opens new vistas in application of soft computing, fuzzy logic and fuzzy set theory to the human sciences. This book is likely to be viewed in retrospect as a landmark in its field" - Lotfi A. Zadeh, University of California, Berkeley.

"Niskanen ... breaks new ground in soft computing, and his book is one of just a few studies that focus on soft computing methods and fuzzy logic with respect to applications in the human sciences, namely, the social and behavioral sciences. ... Numerous figures and tables provide useful information throughout. Summing Up: Highly recommended. Upper-division undergraduates through faculty." (D. J. Gougeon, CHOICE, April, 2004)

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Published: 15th December 2010

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Towards Novel Methods in the Human Sciences	p. 1
Western Methodological Traditions	p. 2
In the Beginning, There Was Imprecision	p. 5
What Is Soft in Soft Computing?	p. 8
Justifications for Soft Computing Models	p. 9
Brief Introduction to Fuzzy Set Theory	p. 14
Fuzzy Sets	p. 14
Basic Operations of Fuzzy Sets	p. 17
Fuzzy Sets and Probability	p. 21
Fuzzy Relations and Graphs	p. 23
Compositions of Fuzzy Relations	p. 25
Fuzzy Graphs	p. 28
Fuzzy Functions	p. 30
Concise Logopedics for Soft Computing Models	p. 35
Quasi-Natural Language	p. 36
Syntactic Aspects	p. 36
Semantic and Pragmatic Aspects	p. 43
Linguistic Approximation	p. 47
The Approximate Truth about the Degrees of Truth	p. 49
A Brief Exegesis of Truth	p. 49
Two Conceptions of Truth	p. 49
Information, Knowledge and Truth	p. 49
Sorites Paradox and Truth	p. 50
Probability and Truth	p. 51
Fuzzy Metric Truth	p. 53
Atomic Sentences	p. 53
Negation and Compound Sentences	p. 57
From Truth to Linguistic Values	p. 59
Approximate Quantifiers	p. 61
Approximate Reasoning	p. 63
Principal Types of Reasoning	p. 63
Fuzzy Reasoning	p. 65
Prevailing Fuzzy Reasoning	p. 68
FMT Reasoning with Prevailing Algorithms	p. 72
Generalized Modus Ponens Reasoning	p. 75
Quantitative Data Examination	p. 84
Soft Computing Approach to Descriptive Statistics	p. 84
Soft Measurement	p. 85
Soft Data Compression	p. 86
Soft Computing Approach to Statistics Calculation	p. 95
Averages	p. 95
Standard Deviation and Variance	p. 100
Soft Inferential Statistics	p. 103
Typical Soft Random Distributions	p. 104
Soft Statistical Hypothesis Assessment	p. 109
Entailment and Causality	p. 111
Contingency Tables	p. 112
Elaboration models	p. 123
Regression Models, Preliminary Considerations	p. 134
Regression Model with One Independent Variable	p. 137
Regression Model with Two Independent Variables	p. 145
Criteria for Assessing the Goodness of Soft Computing Models	p. 149
Regression Models with Training, Control and Validation Data	p. 152
Example of Tuning with Control Data	p. 154
Models Based on Analysis of Variance	p. 174
Conventional Analysis of Variance	p. 174
Analysis of Variance in Soft Computing Model Goodness Assessments	p. 183
Soft Computing Approach to Analysis of Variance	p. 186
Soft Computing Models for Complex Systems	p. 198
A Simple Feedback System	p. 198
Two Classic Problems in Reasoning	p. 207
Sorites Paradox	p. 207
Prisoner's Dilemma	p. 211
An Adaptive Fuzzy Cognitive Map	p. 215
Cognitive Maps and Virtual Worlds	p. 219
Reasoning with Hypotheses	p. 223
Methodological Aspects of Hypotheses	p. 223
Soft Computing and Hypothetico-Deductive Reasoning	p. 231
Towards Soft Computing Applications in Qualitative Research	p. 237
Qualitative Models and Hypermedia	p. 237
Challenges to Computer Models--How Can We Apply Qualitative Research?	p. 239
Challenges to Qualitative Research--How Can We Apply Soft Computing Methods?	p. 245
Conclusions	p. 249
List of Figures	p. 252
List of Tables	p. 260
List of Boxes	p. 262
References	p. 264
Subject Index	p. 270
Table of Contents provided by Rittenhouse. All Rights Reserved.

Studies in Fuzziness and Soft Computing

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