title: 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.
authors.affiliation: Department of Automatic Control, Faculty of Electrical Engineering, University of Banja Luka Patre 5, 78000 Banja Luka, Bosnia and Herzegovina. Electronic address: dino.kosic@etfbl.net.
authors.forename: Dino
authors.initials: D
authors.lastname: Kosic
mesh_terms: D000465:Algorithms; D016018:Least-Squares Analysis; D016571:Neural Networks, Computer
keywords: Adaptive predictive control; Artificial neural network; Least squares; Radial basis function network