5 edition of **Neural Networks and Soft Computing** found in the catalog.

- 106 Want to read
- 10 Currently reading

Published
**April 10, 2003**
by Physica-Verlag Heidelberg
.

Written in English

- Neural networks,
- Congresses,
- Computer Books: General,
- Computers,
- Computers - General Information,
- Soft computing,
- Robotics,
- Neural networks (Computer scie,
- Artificial Intelligence - General,
- Computers / Artificial Intelligence,
- Computer Bks - General Information,
- Neural networks (Computer science)

**Edition Notes**

Contributions | Leszek Rutkowski (Editor), Janusz Kacprzyk (Editor) |

The Physical Object | |
---|---|

Format | Paperback |

Number of Pages | 914 |

ID Numbers | |

Open Library | OL9103726M |

ISBN 10 | 3790800058 |

ISBN 10 | 9783790800050 |

Fuzzy sets, neural networks, and soft computing. New York: Van Nostrand Reinhold, © (OCoLC) Online version: Fuzzy sets, neural networks, and soft computing. New York: Van Nostrand Reinhold, © (OCoLC) Material Type: Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors: Ronald R. Fundamentals of neural networks and various learning methods will then be discussed. The principles of multi-layer feed forward neural network, radial basis function network, self-organizing map, counter-propagation neural network, recurrent neural network, deep learning neural network will be explained with appropriate numerical examples.

Neural networks are parallel computing devices, which are basically an attempt to make a computer model of the brain. The main objective is to develop a system to perform various computational tasks faster than the traditional systems. This tutorial covers the basic concept and terminologies involved in Artificial Neural Size: KB. Neural Networks, Fuzzy Logic, And Genetic Algorithms: Synthesis And Applications (With Cd - Rom) (Computer) is a book that explains a whole consortium of technologies underlying the soft computing which is a new concept that is emerging in computational intelligence/5(24).

Artificial Neural Network (ANN) is an efficient computing system whose central theme is borrowed from the analogy of biological neural networks. ANNs are also named as “artificial neural systems,” or “parallel distributed processing systems,” or “connectionist systems.”. NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS: SYNTHESIS AND APPLICATIONS - Ebook written by S. RAJASEKARAN, G.A. VIJAYALAKSHMI PAI. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS: /5(2).

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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 by: Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods.

It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. The book contains contributions from internationally recognized scientists, such as Zadeh, Bubnicki, Pawlak, Amari, Batyrshin, Hirota.

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.

Neural Networks and Computing Neural Networks and Computing Book Description: This book covers neural networks with special emphasis on. The book aims to give an in-depth, yet easy to read, introduction into Soft Computing and the main techniques employed by geo-scientists. A CD and DVD with OpendTect + plugins and a 3D seismic data set + wells respectively are included for hands-on experimentation with neural networks.

Recurrent Neural Networks and Soft Computing How we measure 'reads' A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a Author: Igor Belič.

Description: This book provides comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence. The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as neuro-fuzzy, fuzzy-genetic, and neuro-genetic systems.

Soft Computing course 42 hours, lecture notes, slides in pdf format; Topics: Introduction, Neural network, Back propagation network, Associative memory, Adaptive resonance theory, Fuzzy set theory, Fuzzy systems, Genetic algorithms, Hybrid systems.

I have a rather vast collection of neural net books. Many of the books hit the presses in the s after the PDP books got neural nets kick started again in the late s.

Among my favorites: Neural Networks for Pattern Recognition, Christopher. Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide.

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.

This volume presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods.

It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. Buy Soft Computing: Fuzzy Logic, Neural Networks, and Distributed Artificial Intelligence by Fred Aminzadeh (Editor), Mohammad Jamshidi (Editor) online at Alibris.

We have new and used copies available, in 1 editions - starting at $ Shop now. Neural Networks is an integral component fo the ubiquitous soft computing paradigm. An in-depth understanding of this field requires some background of the principles of neuroscience, mathematics.

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/5(7). Gómez-Pérez P, Caldeirinha R, Fernandes T and Cuiñas I () Using artificial neural networks to scale and infer vegetation media phase functions, Neural Computing and Applications,(), Online publication date: 1-Jun Introduction to Soft Computing, which aims to exploit tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve close resemblance to humanlike decision making Her interests include artificial intelligence, artificial neural networks, evolutionary algorithms, and cognitive science.

She is an author /5(34). 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 Reviews: 1. Soft computing is an emerging approach to computing which parallel the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision.

Soft computing is based on some biological inspired methodologies such as genetics, evolution, ant’s behaviors, particles swarming, human nervous systems, ification Techniques: Problmes for Practice. Soft computing paradigms such as fuzzy logic system, neural networks and genetic algorithms are discussed in detail with many solved examples to facilitate the in-depth understanding of the Author: Mrutyunjaya Panda.

Vojislav Kecman, “Learning & Soft Computing Support Vector Machines, Neural Networks, and Fuzzy Logic Models”, Pearson Education, New Delhi, [7] S.

Rajasekaran & A Vijayalakshmi Pai “Neural Networks, uzzy Logic, and enetic.Reviewer: John A. Fulcher It seems appropriate to begin this review of books on neural networks by establishing the scope of what is to be covered.

First, it does not include the classic references in the field (some of which have been reviewed separately in Computing Reviews) such as Anderson and Rosenfeld [1], Minsky and Papert [2], Kohonen [3], and Rumelhart and McClelland [4,5].Book 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.