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Sinopsis
Intelligence systems. We perfonn routine tasks on a daily basis, as for example: * recognition of faces of persons (also faces not seen for many years), * identification of dangerous situations during car driving, * deciding to buy or sell stock, * reading hand-written symbols, * discriminating between vines made from Sauvignon Blanc, Syrah or Merlot grapes, and others. Human experts carry out the following: * diagnosing diseases, * localizing faults in electronic circuits, * optimal moves in chess games. It is possible to design artificial systems to replace or "duplicate" the human expert. There are many possible definitions of intelligence systems. One of them is that: an intelligence system is a system able to make decisions that would be regarded as intelligent ifthey were observed in humans. Intelligence systems adapt themselves using some example situations (inputs of a system) and their correct decisions (system's output). The system after this learning phase can make decisions automatically for future situations. This system can also perfonn tasks difficult or impossible to do for humans, as for example: compression of signals and digital channel equalization.
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Reseña del editor
Intelligence systems. We perfonn routine tasks on a daily basis, as for example: · recognition of faces of persons (also faces not seen for many years), · identification of dangerous situations during car driving, · deciding to buy or sell stock, · reading hand-written symbols, · discriminating between vines made from Sauvignon Blanc, Syrah or Merlot grapes, and others. Human experts carry out the following: · diagnosing diseases, · localizing faults in electronic circuits, · optimal moves in chess games. It is possible to design artificial systems to replace or "duplicate" the human expert. There are many possible definitions of intelligence systems. One of them is that: an intelligence system is a system able to make decisions that would be regarded as intelligent ifthey were observed in humans. Intelligence systems adapt themselves using some example situations (inputs of a system) and their correct decisions (system's output). The system after this learning phase can make decisions automatically for future situations. This system can also perfonn tasks difficult or impossible to do for humans, as for example: compression of signals and digital channel equalization.
Reseña del editor
The book provides an introduction to basic concepts as well as some recent advancements in fuzzy set theory, approximate reasoning, artificial neural networks and clustering methods. These methodologies create together the so-called soft computing, which is part of a computational approach to system intelligence. The book deals with an overview of fuzzy set theory, foundations for approximate reasoning principles, specific equivalence of inference results using logical conjunctive interpretations of if-then rules, supervised and unsupervised artificial neural networks, a new generalized conditional fuzzy clustering method, artificial neural networks-based fuzzy inference system with parameterized consequences in if-then rules, MATLAB m-files implementation of neuro-fuzzy systems, detailed study of neuro-fuzzy systems applications.
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