文章基本信息

标题：Shuffled Frog-Leaping Algorithm trained RBFNN Equalizer
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作者：Pradyumna Mohapatra ; Padma C. Sahu ; K. Parvathi 等
期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2017
卷号：9
页码：249-256
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：Ability of Artificial Neural Networks (ANN) in mapping between the variables attracts its application in channel equalization. Single hidden layer of Radial Basis function Neural Networks (RBFNN) makes it most popular equalizers to mitigate the channel distortions. Most challenging problem associated with design of RBFNN Equalizer is the traditional hit and trial method. Ability of evolutionary algorithms in solving complex problems in finding global optimal solutions attracted this paper for training of RBFNN equalizer using a recently proposed population based optimization, Shuffled Frog-Leaping Algorithm (SFLA) and three of its modified forms. It is found from the simulation results that performances of different forms of SFLA for the training of RBFNN equalizers are superior as compared to existing equalizers.
关键词：Radial Basis Function Neural Network; Channel Equalization; Shuffled Frog-Leaping Algorithm.