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Process Neural Network (PNN) has an important significance in solving industry modeling problems which are related to time, but long time is cost on high dimension inputs nonlinear modeling problems. A new Improved Process Neural Networks based on KPCA and Walsh (IPNN-KPW) are proposed in this paper. KPCA method and discrete Walsh transform are used to reduce process neural network's time cost. Momentum factor and self-adapting learning rate are adopted to accelerate the astringency of the network and keep down network's oscillation. The IPNN-KPW is applied to modeling of Polyacrylonitrile (PAN) average molecular weight in polymerization. The effectiveness of the algorithm is verified by the results. A higher accuracy of model is obtained with less time.
Date of Conference: 11-13 Dec. 2009