Probabilistic neural-network structure determination for pattern classification

Network structure determination is an important issue in pattern classifica tion based on a probabilistic neural network. In this study, a supervised n etwork structure determination algorithm is proposed. The proposed algorith m consists of two parts and runs in an iterative way. The first part identi fies an appropriate smoothing parameter using a genetic algorithm, while th e second part determines suitable pattern layer neurons using a forward reg ression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy .