基于模擬退火遺傳算法的模糊分類器參數(shù)優(yōu)化及其應(yīng)用
THE PARAMETER OPTIMIZATION OF MMNN BASED ON GENETIC ALGORITHM COMBINED WITH SIMULATED ANNEALING AND ITS APPLICATION
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摘要: 該文從結(jié)構(gòu)和算法上研究了Max-Min模糊神經(jīng)網(wǎng)絡(luò)(MMNN),找出了其固有的局限性,相應(yīng)提出了一系列的改進(jìn)措施形成改進(jìn)MMNN算法。為了更好地提高網(wǎng)絡(luò)的性能,同時(shí)考慮到優(yōu)化算法的收斂速度,本文提出了基于模擬退火遺傳算法的網(wǎng)絡(luò)參數(shù)優(yōu)化方法,通過(guò)計(jì)算機(jī)仿真,證明了該方法是可行的。最后,運(yùn)用它作為分類器對(duì)實(shí)際的船舶輻射噪聲進(jìn)行了分類實(shí)驗(yàn),與BP等算法進(jìn)行了比較,顯示出其獨(dú)特的優(yōu)越性。Abstract: In this paper, the structure and algorithm of Max-Min fuzzy neural network (MMNN) are studied in detail. In order to get rid of some intrinsic localization of the method and boost up the capability of the MMNN, a series of steps are presented and the improved project (IMMNN) is gained. With a view to making the capability even much better and compressing the time of the convergence, the op-IMMNN is put forward in which the parameters of IMMNN are optimized by genetic algorithm combined with simulated annealing. In the simula- tion, the result of op-IMMNN is superior over the conventional MMNNs. Finally, a satisfactory result is also obtained when op-IMMNN is regarded as a classifier to distinguish the types of the ships according to their actual radiated noise. Comparing with the neural network based on the back propagation algorithm, the advantages of the op-IMMNN are fully put up.
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P.K. Simpson, Fuzzy Min-Max networks-part 1, Classification, IEEE Trans. on Neural Networks,1992, NN-3(5), 776-786.[2]P.K. Simpson, Fuzzy Min-Max networks-part 2, Clustering, IEEE Trans. on Fuzzy Systems,1993, 1(1), 32-45.[3]張講社,徐宗本等,整體退火遺傳算法及其收斂充要條件,中國(guó)科學(xué)E輯,1997,27(2),154 1G4[4]張濤,文學(xué)章,吸引子維數(shù)計(jì)算的幾點(diǎn)改進(jìn),浙江大學(xué)學(xué)報(bào)(自然科學(xué)版),1993,27(5),673-679[5]Hisao Ishibuchi, KenNozahi, Selecting fuzzy if-then rules for classification problems using genetic algorithm, IEEE Trans. on Fuzzy Systems, 1995, 3(3), 260 270. -
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