Wu, Qingxiang (2010) Computing Network Models for Intelligent Systems--Hybrid of neural networks, multi-knowledge, fuzzy logic, rough set, and Bayesian classifier. VDM Verlag Dr. Muller Aktiengesellschaft & Co. KG. 282 pp ISBN 978-3-639-22560-0 [Book (authored)]
Indefinitely restricted to Repository staff only.
Humans 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, Bayesian 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.
|Item Type:||Book (authored)|
|Keywords:||Computing networks, spiking neural networks, machine learning, intelligent systems, multi-knowledge, decision making, rough sets, Bayes classifier, Fuzzy logics, robot|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
|Deposited By:||Dr Qingxiang Wu|
|Deposited On:||30 Jan 2012 16:07|
|Last Modified:||09 Dec 2015 11:00|
Repository Staff Only: item control page