NDLI: Multiple-prior-knowledge neural network for industrial processes

Content Provider	IEEE Xplore Digital Library
Author	Lou Haichuan Su Hongye Xie Lei Gu Yong Rong Gang
Copyright Year	2010
Description	Author affiliation: National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China (Lou Haichuan; Su Hongye; Xie Lei; Gu Yong; Rong Gang)
Abstract	A novel multiple-prior-knowledge neural network for industrial processes is proposed. Diversity of priori knowledge from industrial processes are discovered and embedded into three-layer hybrid feedforward neural network in the form of penalty function or nonlinear constraints. Meanwhile, Sequential quadratic programming method is selected as the network learning algorithm. Compared with BP neural network and an industrial neural network (the Bounded Derivative neural network, BDNN), MPKN network not only satisfies a certain process mechanism and keeps the process safety, but also has better generalization. Its effective performance is validated with the modelling of a nonlinear process of Continuous-stirred tank reactor.
Starting Page	385
Ending Page	390
File Size	853688
Page Count	6
File Format	PDF
ISBN	9781424483754
e-ISBN	9781424483761
e-ISBN	9781424483747
DOI	10.1109/ICAL.2010.5585314
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-16
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Training Sequential quadratic programming (SQP) Nonlinear modeling Industrial processes Fitting Artificial neural networks Predictive models Multiple-Prior-Knowledge neural network (MPKNN) Steady-state Feedforward neural networks
Content Type	Text
Resource Type	Article

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