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

QingXiang Wu. Computing Network Model for Intelligent Systems: Hybrid of neural networks, multi-knowledge, fuzzy logic, rough set, and Bayesian classifier Authors:QingXiang Wu.
Publisher:
Year:2010
Serie:
Pages:292
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Price:11822 rub.
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Book Summary:
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...

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