Compensatory Genetic Fuzzy Neural Networks and Their Applications
This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms, and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games, and pattern recognition. The proposed soft computing system is effective in performing both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also presents various novel soft computing techniques.
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Fuzzy Compensation Principles
Normal Fuzzy Reasoning Methodology
Compensatory Genetic Fuzzy Neural Networks
Fuzzy Knowledge Rediscovery in Fuzzy Rule Bases
Fuzzy Cartpole Balancing Control Systems
Fuzzy Knowledge Compression and Expansion
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ANFIS Boolean logic Boolean truth table cart-pole balancing system CFNN CNFs and DNFs commonly used fuzzy conventional fuzzy reasoning dashed line data granularity defined defuzzification Egotist fa(P FNNKD fuzzy control fuzzy game fuzzy IF-THEN rules fuzzy knowledge fuzzy logic fuzzy moves fuzzy neural network fuzzy reasoning method fuzzy reasoning methodology fuzzy rule base fuzzy truth table game transformation functions genetic algorithms GFNN global PDs globally strategic goals goto Step heuristic HGLA hybrid intelligent system Karnaugh map learning algorithm membership functions neuro-fuzzy system neurons normal fuzzy reasoning normal fuzzy system number of fuzzy outcome output fuzzy sets output membership functions player primary fuzzy sets RMSE shown in Figure soft computing system solid line square error sub-concepts t-norm Taichi Takagi-Sugeno's fuzzy system tē a tē training data trapezoidal fuzzy set trapezoidal quaternions trapezoidal-type fuzzy set Wang's fuzzy system Yin and Yang