Topic
Soft computing
About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.
Papers published on a yearly basis
Papers
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TL;DR: Today, the authors have microwave ovens and washing machines that can figure out on their own what settings to use to perform their tasks optimally; cameras that come close to professional photographers in picture-taking ability; and many other products that manifest an impressive capability to reason, make intelligent decisions, and learn from experience.
Abstract: Prof. Zadeh presented a comprehensive lecture on fuzzy logic, neural networks, and soft computing. In addition, he lead a spirited discussion of how these relatively new techniques may be applied to safety evaluation of time variant and nonlinear structures based on identification approaches. The abstract of his lecture is given as follows.
1,390 citations
Book•
01 Jan 1997
TL;DR: Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy Systems.
Abstract: From the Publisher:
Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy systems. Exploring the value of relating genetic algorithms and expert systems to fuzzy and neural technologies, this forward-thinking text highlights an entire range of dynamic possibilities within soft computing. With examples of specifically designed to illuminate key concepts and overcome the obstacles of notation and overly mathematical presentations often encountered in other sources, plus tables, figures, and an up-to-date bibliography, this unique work is both an important reference and a practical guide to neural networks and fuzzy systems.
1,349 citations
Book•
19 Mar 2001
TL;DR: This textbook provides a thorough introduction to the field of learning from experimental data and soft computing and assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole.
Abstract: This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
1,118 citations
TL;DR: A comprehensive review of the work done, during the 1968-2005, in the application of statistical and intelligent techniques to solve the bankruptcy prediction problem faced by banks and firms is presented.
978 citations
TL;DR: The objective of this paper is to provide an account of genetic fuzzy systems, with special attention to genetic fuzzy rule-based systems.
852 citations