HYBRID RECURSIVE PARTICLE SWARM OPTIMIZATION LEARNING ALGORITHM IN THE DESIGN OF RADIAL BASIS FUNCTION NETWORKS
In this paper, an innovative hybrid recursive particle swarm optimization (HRPSO) learning algorithm with normalized fuzzy cmean (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.
Chen, Ching-Yi; Feng, Hsuan-Ming; and Ye, Fun
"HYBRID RECURSIVE PARTICLE SWARM OPTIMIZATION LEARNING ALGORITHM IN THE DESIGN OF RADIAL BASIS FUNCTION NETWORKS,"
Journal of Marine Science and Technology: Vol. 15:
1, Article 5.
Available at: https://jmstt.ntou.edu.tw/journal/vol15/iss1/5