Computing Network Model for Intelligent Systems | Hybrid of neural networks, multi-knowledge, fuzzy logic, rough set, and Bayesian classifier

QingXiang Wu

ISBN 10: 3639225600 / ISBN 13: 9783639225600

Published by VDM Verlag Dr. Müller, 2010

About this Item

Computing Network Model for Intelligent Systems | Hybrid of neural networks, multi-knowledge, fuzzy logic, rough set, and Bayesian classifier | QingXiang Wu | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639225600 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 101378791

Bibliographic Details

• Title: Computing Network Model for Intelligent Systems | Hybrid of neural networks, multi-knowledge, fuzzy logic, rough set, and Bayesian classifier
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2010
• Language: English
• ISBN 10: 3639225600
• ISBN 13: 9783639225600
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 17D millimeters 220 millimeters width by 150 millimeters height by 17 millimeters depth
• Weight: 453 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Scientists mimicked birds and eventually created airplanes using integration of biological principles and modern science and technology. In recent decades scientists have been trying to simulate intelligence in the brain, in which huge number of neurons forms powerful computing networks to perform intelligent behaviours. This book presented a framework of computing network models for artificial intelligent systems to mimic intelligent behaviours. The models are inspired from some biological principles, and furthermore they have been enhanced using hybrid of current artificial intelligent techniques such as machine learning, neural networks, multi-knowledge, fuzzy logic, rough set, Bayes classifier, and evidence reasoning theory. The key idea of the book is to encourage scientists to take more biological findings to build artificial intelligent systems. More importantly biologically inspired models should be extended to combine current artificial intelligent techniques to achieve high level intelligence in some specific aspects. The book presents a demonstration of the effort in implementation of intelligent behaviours using computing networks.