Structure optimization of fuzzy neural network by genetic algorithm
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolution-based design of neural fuzzy networks using self-adapting genetic parameters
IEEE Transactions on Fuzzy Systems
Evolutionary fuzzy decision model for construction management using support vector machine
Expert Systems with Applications: An International Journal
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Genetic algorithms (GAs), fuzzy logic (FL), and neural networks (NNs) are frequently used artificial intelligence (AI) techniques. Since these three methods are complementary rather than competitive, many researchers have hybridized GAs, FL, and NNs to develop a better performance model. However, most hybrid models use a multistage combination or identify partial parameters required in the model resulting in sub-optimal solutions. This research fuses GAs, FL, and NNs to develop an evolutionary fuzzy neural inference model (EFNIM) that uses GAs to simultaneously search for all parameters required in fuzzy neural networks (FNNs). Two approaches, summit and width representation method (SWRM) and block-representation method (BRM), are proposed to encode variables in FL and NNs. Simulations are conducted to evaluate the performance of EFNIM. For different problems, membership functions (MFs) with the minimum FNN structure and optimal parameters of FNN are automatically and concurrently acquired using EFNIM. The research overcomes the difficulties faced in applying FL and NNs as well as saves efforts in trial-and-error experiments, questionnaire survey, interviews with experts, etc. Both prediction accuracy and time requirement for cost estimating are much improved by the proposed method.