Adaptive Training of Radial Basis Function Networks Using Particle Swarm Optimization Algorithm

2005 ◽  
pp. 119-128 ◽  
Author(s):  
Hongkai Ding ◽  
Yunshi Xiao ◽  
Jiguang Yue
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Optimization Algorithm ◽  
Basis Function ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Radial Basis Function Networks ◽  
Swarm Optimization ◽  
Adaptive Training ◽  
Radial Basis

Recursive Particle Swarm Optimization Application for the Radial Basis Function Networks Modeling System

2015 ◽  
Vol 713-715 ◽  
pp. 1817-1820
Author(s):  
Ling Liu ◽  
Min Chen ◽  
Hong Yi Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Basis Functions ◽  
Radial Basis Function Networks ◽  
Historical Knowledge ◽  
Swarm Optimization ◽  
Radial Basis

A Recursive Particle Swarm Optimization (RPSO) is proposed to solve dynamic optimization problems where the data is obtained not once but one by one. The position of each particle swarm is updated recursively based on the continuous data and the historical knowledge. The experiment results indicate that RPSO-based radial basis function networks needs fewer radial basis functions and gives more accurate results than traditional PSO in solving dynamic problems.

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