Abstract:
Process neural network could handle the modeling problems of time related industry, but it needs a long time when the input dimension is high. In this paper, a new improved process neural network based on KPCA and Walsh (IPNNKPW) is proposed. Both KPCA method and discrete Walsh transform are used to reduce the time cost of process neural network. Meanwhile, both momentum factor and selfadapting learning rate are introduced to accelerate the astringency of the network and keep down network′s oscillation. IPNNKPW is applied to model polyacrylonitrile (PAN) average molecular weight in polymerization, whose results verify the effectiveness of the proposed algorithm.