Multilayer feedforward networks are universal approximators
Neural Networks
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Computer
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
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This paper presents the tuning of the structure and parameters of a proposed fuzzy neural network (FNN) using a modified genetic algorithm (GA). A FNN with switches introduced to layer 2-3 and 3-4 links is proposed. By doing this, the proposed FNN can learn both the input-output relationships of an application and the network structure using the modified GA. The number of hidden nodes in layer 3 is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. An application example on sunspot forecasting is given to highlight the merits of the modified GA and the proposed FNN.