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

Qingxiang Wu

    Research output: Book/ReportBook

    Abstract

    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.
    Original languageEnglish
    PublisherOmniScriptum Publishing
    Number of pages282
    ISBN (Print)978-3-639-22560-0
    Publication statusPublished - 10 Jan 2010

    Keywords

    • Computing networks
    • spiking neural networks
    • machine learning
    • intelligent systems
    • multi-knowledge
    • decision making
    • rough sets
    • Bayes classifier
    • Fuzzy logics
    • robot

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