Fuzzy neural network optimization by a multi-objective differential evolution algorithm
pp. 38-43
Abstrakt
Designing a set of fuzzy neural networks can be considered as solving a multi-objective optimization problem. An algorithm for solving the multi-objective optimization problem is presented based on differential evolution through the max-min distance density and a Pareto candidate solution set maintenance method. The search for the Pareto Optimal Set of fuzzy neural networks optimization problems is performed. Numerical simulations for taste identification of tea show that the algorithm is feasible and efficient.
Publication details
Published in:
Cao Bing-yuan, Zhang Cheng-yi, Li Tai-fu (2009) Fuzzy information and Engineering I. Dordrecht, Springer.
Seiten: 38-43
DOI: 10.1007/978-3-540-88914-4_6
Referenz:
Ma Ming, Zhang Li-biao, Xu Xiang-li (2009) „Fuzzy neural network optimization by a multi-objective differential evolution algorithm“, In: B. Cao, C. Zhang & T.-f. Li (eds.), Fuzzy information and Engineering I, Dordrecht, Springer, 38–43.