B-06
Evolving Fuzzy Neural Networks by means of Fuzzy Differential Evolution
Hidehiko Okada
1
genotype [1]
1 Fuzzy
Differential Evolution: FDE
(FNN)[2]
2
[3] [2] FNN FNN 3 FNN 1 FNN 1 1: [2] FNN x O (1)-(5) (1)-(5) , , , , , , , , [2] 2 c w L U [3] 2: [3] 1 FNN [2]3
DE
FNN FNN [3] DE genotype genotype Xi = [xiL, xiU] Xi = (xic, xiw) xiL, xiU, xic, xiw Xi [3] LU CW FDE DE[4] genotype DE DE/rand/1/bingenotype Xa Xa Xa Xa donor vector
trial vector Ya Za Ya 3 Xb1 Xb2 Xb3 Xb1 Xb2 Xb3 DE/rand/1/bin Xa Xb1 Xb2 Xb3 Xa Xa Ya Za Xb1 Xb2 Xb3 LU , Faculty of Computer Science and Engineering, Kyoto Sangyo University
(6),(7) ( CW (9) (6)-(9) F DE/rand/1/bin DE/rand/1/bin DE/rand/1/bin FDE DE donor vector DE/rand/1/bin
4
[5] [2] FNN FDE 3 [3] 3 F0.0L F0.0U F1.0 F(x) [3] FNN =10 FDE =100 =10,000 F=0.5 CR=0.8 genotype =LU FNN FNN 4 FNN 5 4 5 FNN 35
DE NN FNN[1] H. Okada, Proposal of fuzzy evolutionary algorithms with
fuzzy valued genotypes, Proc. of International Conference on Instrumentation, Control and Information Technology (SICE Annual Conference 2012), 1538-1541, 2012. [2] H. Ishibuchi, H. Tanaka and H. Okada, Fuzzy neural
networks with fuzzy weights and fuzzy biases, Proc. of IEEE International Conferences on Neural Networks, 1650-1655, 1993.
[3] , GA
,
25 , C-5, 2013.
[4] R. Storn and K. Price, Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization,11(4), 341-359, 1997.
[5] X. Yao, Evolving artificial neural networks, Proc. of the IEEE, 87(9), 1423-1447, 1999.
3: [3]
4: FNN