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Convergent design of a piecewise linear neural network

A piecewise linear neural network (PLNN) is discussed which maps N-dimensional input vectors into M-dimensional output vectors. A convergent algorithm for designing the PLNN from training data is described The design algorithm is based on a variation of backtracking algorithm known as the 'branch-and-bound' method. The performance of the PLNN is compared with that of a multilayer perceptron (MLP) of equivalent size. The results show that the PLNN is capable of performing as well as an equivalent MLP.