Hybrid Recursive Particle Swarm Optimization Learning Algorithm in the Design of Radial Basis Function Networks
學年 95
學期 2
出版（發表）日期 2007-03-01
作品名稱 Hybrid Recursive Particle Swarm Optimization Learning Algorithm in the Design of Radial Basis Function Networks
作品名稱（其他語言）
著者 Feng, Hsuan-ming; Chen, Ching-yi; 余繁; Ye, Fun
單位 淡江大學電機工程學系
出版者 Springer
著錄名稱、卷期、頁數 Journal of Marine Science and Technology 15(1), pp.31-40
摘要 In this paper, an innovative hybrid recursive particle swarm optimization (HRPSO) learning algorithm with normalized fuzzy c-mean (NFCM) clustering, particle swarm optimization (PSO) and recursive least-squares (RLS) is proposed to generate radial basis function networks (RBFNs) modeling system with small numbers of descriptive radial basis functions (RBFs) for fast approximating two complex and nonlinear functions. Simulation results demonstrate that the generated NFCM-based learning schemes approach the desired modeling systems within the smaller population sizes.
關鍵字 normalized fuzzy c-means; particle swarm optimization; recursive least-squares; radial basis function networks
語言 en
ISSN 0948-4280
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