Fast Clustered Radial Basis Function Network as an adaptive predictive controller

Abstract

This paper presents a novel artificial neural network with the Radial Basis Function (RBF) as an activation function of neurons and clustered neurons in the hidden layer which has a high learning speed, thus it is called Fast Clustered Radial Basis Function Network (FCRBFN). The weights of the network are determined by solving a number of linear equation systems. In addition, new training data can be given to the network on-line and the re-training is done at high speed using the Least Squares method. In order to test the validity of the FCRBFN, it is applied to 4 classical regression applications, and also used to build the functional adaptive predictive controller. Experimental results show that, compared with other methods, the FCRBFN with a small amount of hidden neurons could achieve good or better regression precision and generalization, as well as adaptive ability at a much faster learning speed.