A new structure adaptation algorithm for RBF networks and its application

Authors:
D. L. Yu;D. W. Yu
Affiliations:
Control Systems Research Group, School of Engineering, Liverpool John Moores University, Byrom Street, L3 3AF, Liverpool, UK;Department of Automation, Northeast University at Qinhuangdao, Byrom Street, L3 3AF, Qinhuangdao, China
Venue:
Neural Computing and Applications
Year:
2006

Citing 0
Cited 2

Orthogonal least square center selection technique - A robust scheme for multiple source Partial Discharge pattern recognition using Radial Basis Probabilistic Neural Network

Expert Systems with Applications: An International Journal
Approximation of Gaussian basis functions in the problem of adaptive control of nonlinear objects

Cybernetics and Systems Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

An adaptation algorithm is developed for radial basis function network (RBFN) in this paper. The RBFN is adapted on-line for both model structure and parameters with measurement data. When the RBFN is used to model a non-linear dynamic system, the structure is adapted to model abrupt change of system operating region, while the weights are adapted to model the incipient time varying parameters. Two new algorithms are proposed for adding new centres while the redundant centres are pruned, which is particularly useful for model-based control. The developed algorithm is evaluated by modelling a numerical example and a chemical reactor rig. The performance is compared with a non-adaptive model.