Best match algorithm with radial basis functions (BMRBF)

Summary form only given, as follows. The authors discuss an algorithm for implementation of a best match radial basis function (BMRBF) neural network. The algorithm is based on the modified Kullback-Leibler information number with nearest-neighbor density estimate and the well-known supervised least mean square (LMS) method. The network architecture is a single hidden layer with RBF nodes. BMRBF is easy to implement and time-efficient and can be used for chaotic time series prediction and classification