Paper, Intelligent Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms, Ch.1, pp.1–33,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
