A MINIMUM ERROR NEURAL-NETWORK (MNN)

A minimum error neural network (MNN) model is presented and applied to a network of the appropriate architecture. The associated one-pass learning rule involves the estimation of input densities. This is accomplished by utilizing local Gaussian functions. A major distinction between this network and other Gaussian based estimators is in the selection of covariance matrices. In MNN, every single local function has its own covariance matrix. The Gram-Schmidt orthogonalization process is used to obtain these matrices. In comparison with the well known probabilistic neural network (PNN), the proposed network has shown improved performance.