The applicability of the modified probabilistic neural network to channel equalization can be severely limited by the size of the network. The size of the network grows exponentially with the order of the channel and the dimension of the input vectors. As a result, the standard network is practical only for low order channels with small input alphabet size. An algorithm is proposed to alleviate such an undesirable constraint by finding a much smaller network representation with a similar decision surface.
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