RBF neural network for probability density function estimation and detecting changes in multivariate processes

We propose a new radial basis function (RBF) neural network for probability density function estimation. This network is used for detecting changes in multivariate processes. The performance of the proposed model is tested in terms of the average run lengths (ARL), i.e., the average time delays of the change detection. The network allows the processing of large streams of data, memorizing only a small part of them. The advantage of the proposed approach is in the short and reliable net training phase.