The united adaptive learning algorithm for the link weights and shape parameter in RBFN for pattern recognition

This paper proposes a united training method of the link weights of the Gaussian radial basis function networks (GRBFN) and the shape parameter or of the RBF. The training method corresponding to the former is a kind of recursive least squares backpropagation (RLS-BP) learning algorithm which is an accurately recursive method, the training method corresponding to the latter is an adaptive gradient descending (AGD) searching algorithm which is an approximately approaching method. We use the one-dimensional images of radar targets to study the effect of the shape parameter alpha on the rate of recognition, and survey the changes of the shape parameter alpha s of radial basis functions corresponding to different hidden nodes, and present the judgement confidence curves of different radar targets. In addition, the forgotten factor lambda which makes the effects on the speed of convergence is also discussed. The experimental results are presented.