Optimization of EBFN architecture by an improved RPCL algorithm with application to process control

EBF networks are an extension of radial basis function (RBF) networks. Selecting an appropriate number of clusters is a problem for RBF or EBF networks. The rival penalized competitive learning (RPCL) algorithm is designed to solve this problem but its performance is not satisfactory when the data has overlapped clusters and the input vectors contain dependent components. The paper addresses this problem by incorporating full covariance matrices into the original RPCL algorithm. The resulting algorithm, referred to as the improved RPCL algorithm progressively eliminates the units whose clusters contain only a small portion of the training data. The improved algorithm is applied to optimize the architecture of elliptical basis function networks for process control. The results show that the covariance matrices in the improved RPCL algorithm have a better representation of the clusters.