Overview
- The first book to integrate the evolutionary, fuzzy-logic, and neural-network approaches to information processing in a well-balanced way, at the text book level
- Numerous detailed examples
- Includes supplementary material: sn.pub/extras
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About this book
Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically.
This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as
This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as
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Keywords
Table of contents (9 chapters)
Reviews
From the reviews:
"The authors explain that soft computing is a term covering such computing methodologies which are, in some sense, tolerant of imprecision, uncertainty and partial truth. There are three of them exposed in this book, namely: fuzzy logic, evolutionary algorithms and artificial neural networks – all inspired by nature. … The book contains many examples of applications, which cover a broad spectrum: finance, medicine, robotics and others." (Jerzy Mycka, Zentralblatt MATH, Vol. 981, 2002)
"The reviewed book is intended both for undergraduate students in soft computing disciplines as well as for engineers, practitioners, and problem solvers in many areas of application. … The main theme of the reviewed book is integration and synergistic co-operation of a few soft-computing methodologies that have their roots in neural systems. … I fully agree with the authors that several techniques presented can be used sequentially to solve parts of the problem in a ‘pipeline’ fashion, because it … will be used increasingly." (Dr. Ing. Miroslav Svítek, Neural Network World, Vol. 12 (2), 2002)
"The book Soft Computing: Integrating Evolutionary, Neural and Fuzzy Systems provides a comprehensive introduction to the area of soft computing addressing three of the main constituents of this discipline: fuzzy logic, neural computing and evolutionary computing. … The book is primarily aimed at undergraduate students and practitioners in the field. The book is organized in a modular form which enable readers to select their own pathway through the chapters. … The academic content is well supported by informative practical examples and case studies." (Liam Maguire, Artificial Intelligence Review, Vol. 23, 2006)
Authors and Affiliations
Bibliographic Information
Book Title: Soft Computing
Book Subtitle: Integrating Evolutionary, Neural, and Fuzzy Systems
Authors: Andrea Tettamanzi, Marco Tomassini
DOI: https://doi.org/10.1007/978-3-662-04335-6
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2001
Hardcover ISBN: 978-3-540-42204-4Published: 07 September 2001
Softcover ISBN: 978-3-642-07583-4Published: 09 December 2010
eBook ISBN: 978-3-662-04335-6Published: 17 April 2013
Edition Number: 1
Number of Pages: XIII, 328
Topics: Mathematical Logic and Foundations, Artificial Intelligence, Computation by Abstract Devices, Algorithm Analysis and Problem Complexity, Mathematical Logic and Formal Languages, Complexity