A proposal for an artificial neural network that optimizes reference vectors: FMNET

A new artificial layered neural network model called FMNET (feature mapping and matching network) is proposed. FMNET has a feature mapping layer (F-layer) and a subsequent matching layer (M-layer). The F-layer maps the training set to the univariate Gaussian form and the M-layer creates or integrates the output neurons under the likelihood criterion to attain the unimodal Gaussian form. The well optimized FMNET extracts the feature vectors as the expectation value of the output vectors of the F-layer, and the backpropagation learning method becomes consistent with the maximum likelihood estimation method in an asymptotic condition. Furthermore, a good generalizing property is attained by an experiment using mixed Gaussian test patterns