Probabilistic neural-network structure determination for pattern classification.
Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy.
Mao
K Z
KZ
Centre for Signal Processing, Nanyang Technological University, Singapore.
Tan
K C
KC
Ser
W
W