基于模拟退火遗传算法的模糊分类器参数优化及其应用
THE PARAMETER OPTIMIZATION OF MMNN BASED ON GENETIC ALGORITHM COMBINED WITH SIMULATED ANNEALING AND ITS APPLICATION
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摘要: 该文从结构和算法上研究了Max-Min模糊神经网络(MMNN),找出了其固有的局限性,相应提出了一系列的改进措施形成改进MMNN算法。为了更好地提高网络的性能,同时考虑到优化算法的收敛速度,本文提出了基于模拟退火遗传算法的网络参数优化方法,通过计算机仿真,证明了该方法是可行的。最后,运用它作为分类器对实际的船舶辐射噪声进行了分类实验,与BP等算法进行了比较,显示出其独特的优越性。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]张讲社,徐宗本等,整体退火遗传算法及其收敛充要条件,中国科学E辑,1997,27(2),154 1G4[4]张涛,文学章,吸引子维数计算的几点改进,浙江大学学报(自然科学版),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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