<p><em>Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic</em><em>Algorithms</em> is an organized edited collection of contributed chapters covering basic principles, methodologies, and applications of fuzzy systems, neural networks and genetic algorithms. All chapters are origina
Introduction to Fuzzy Systems, Neural Networks, and Genetic Algorithms
โ Scribed by Hideyuki T.
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โฆ Synopsis
Paper, Intelligent Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms, Ch.1, pp.1โ33,USA, September, 1997
Introduction
Fuzzy theory and systems
Aspects of fuzzy systems
Mathematical model-based control and rule-based control
Design of antecedent parts
Design of consequent parts
Fuzzy reasoning and aggregation
Analogy from biological neural networks
Several types of artificial neural networks
Feed-forward NN and the backpropagation learning algorithm
Function approximation
Evolutionary computation
GA as a searching method
GA operations
GA operation: selection
GA operation: crossover
GA operation: mutation
Designing FSs using NN or GA
NN configuration based on fuzzy rule base
Combination of NN and FS
NN learning and configuration based on GA
NN-based fitness function for GA
โฆ Subjects
ะะฝัะพัะผะฐัะธะบะฐ ะธ ะฒััะธัะปะธัะตะปัะฝะฐั ัะตั ะฝะธะบะฐ;ะัะบััััะฒะตะฝะฝัะน ะธะฝัะตะปะปะตะบั;ะญะฒะพะปััะธะพะฝะฝัะต ะฐะปะณะพัะธัะผั
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