Evolutionary Pattern Recognition in Incomplete Nonlinear Multithreshold Networks

A pattern recognition network which computes a weighted sum of nonlinear functions of its inputs is considered. An algorithm for training this multithreshold network is presented. The multithreshold device is used for classifying patterns into more than two categories. Experimental results on the recognition of hand-printed characters are shown. In this case, the nonlinear functions consisted of an orthogonal set of property detectors. The network changed its structure by an evolutionary technique which consisted of periodic replacement of the least useful elements by new ones, randomly chosen.