Rough-Neural Computing: Techniques for Computing with Words

Overview

Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g., in decision making, classification or learning; among these paradigms are fuzzy sets, rough sets, neural networks, and genetic algorithms.

It is well understood now in the soft computing community that hybrid approaches combining various paradigms provide very promising attempts to solving complex problems. Exploiting the potential and strength of both neural networks and ...

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Overview

Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g., in decision making, classification or learning; among these paradigms are fuzzy sets, rough sets, neural networks, and genetic algorithms.

It is well understood now in the soft computing community that hybrid approaches combining various paradigms provide very promising attempts to solving complex problems. Exploiting the potential and strength of both neural networks and rough sets, this book is devoted to rough-neurocomputing which is also related to the novel aspect of computing based on information granulation, in particular to computing with words. It provides foundational and methodological issues as well as applications in various fields.

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Editorial Reviews

From the Publisher

From the reviews:

"This book (actually edited volume) presents recent advances in the area of rough neural computer from a wide konge (sic) of perspectives. … The book contains a few introductory articles suitable to understand what the field of rough several computing is about." (Jan Friso Groote, Zentralblatt MATH, Vol. 1040(9), 2004)

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Product Details

  • ISBN-13: 9783540430599
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 11/13/2003
  • Series: Cognitive Technologies Series
  • Edition description: 2004
  • Edition number: 1
  • Pages: 736
  • Product dimensions: 6.26 (w) x 9.40 (h) x 1.28 (d)

Table of Contents

1 Elementary Rough Set Granules: Toward a Rough Set Processor 5
2 Rough-Neural Computing: An Introduction 15
3 Information Granules and Rough-Neural Computing 43
4 A Rough-Neural Computation Model Based on Rough Mereology 85
5 Knowledge-Based Networking in Granular Worlds 109
6 Adaptive Aspects of Combining Approximation Spaces 139
7 Algebras from Rough Sets 157
8 Approximation Transducers and Trees: A Technique for Combining Rough and Crisp Knowledge 189
9 Using Contextually Closed Queries for Local Closed-World Reasoning in Rough Knowledge Databases 219
10 On Model Evaluation, Indexes of Importance, and Interaction Values in Rough Set Analysis 251
11 New Fuzzy Rough Sets Based on Certainty Qualification 277
12 Toward Rough Datalog: Embedding Rough Sets in Prolog 297
13 On Exploring Soft Discretization of Continuous Attributes 333
14 Rough-SOM with Fuzzy Discretization 351
15 Biomedical Inference: A Semantic Model 373
16 Fundamental Mathematical Notions of the Theory of Socially Embedded Games: A Granular Computing Perspective 411
17 Fuzzy Games and Equilibria: The Perspective of the General Theory of Games on Nash and Normative Equilibria 435
18 Rough Neurons: Petri Net Models and Applications 471
19 Information Granulation in a Decision-Theoretical Model of Rough Sets 491
20 Intelligent Acquisition of Audio Signals Employing Neural Networks and Rough Set Algorithms 521
21 An Approach to Imbalanced Data Sets Based on Changing Rule Strength 543
22 Rough-Neural Approach to Testing the Influence of Visual Cues on Surround Sound Perception 555
23 Handwritten Digit Recognition Using Adaptive Classifier Construction Techniques 573
24 From Rough through Fuzzy to Crisp Concepts: Case Study on Image Color Temperature Description 587
25 Information Granulation and Pattern Recognition 599
26 Computational Analysus of Acquired Dyslexia of Kanji Characters Based on Conventional and Rough Neural Networks 637
27 WaRS: A Method for Signal Classification 649
28 A Hybrid Model for Rule Discovery in Data 689
Author Index 723
Index 725
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