An improved particle swarm optimization algorithm for radial basis function neural network

Authors:
Duan Qichang;Zhao Min;Duan Pan
Affiliations:
College of Automation, Chong qing University, Chongqing;College of Automation, Chong qing University, Chongqing;College of Electrical Engineering, Chong qing University, Chongqing
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 5
Cited 0

Universal approximation using radial-basis-function networks

Neural Computation
Fast learning in networks of locally-tuned processing units

Neural Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation
Robust nonlinear model identification methods using forward regression

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

An improved particle swarm optimization (IMPSO) which synthesizes the existing models of constriction factor approach (CF A PSO) is proposed. In the proposed method, an adaptive algorithm based on the search space adjustable is applied to solve the problem that conventional particle swarm optimization (PSO) algorithm easily falls into local optimal and occur premature convergence. Then, the IMPSO is used to optimize the parameters of RBF neural network. The new training algorithm is used to approximate polynomial function and predict chaotic time series, compared with PSO, and CF A PSO, the algorithm speed up the speed of convergence, and has much greater accuracy.