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Optimization of a Multilayer Neural Network by Using Minimal Redundancy Maximal Relevance-Partial Mutual Information Clustering With Least Square Regression

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Optimization of a Multilayer Neural Network by Using Minimal Redundancy Maximal Relevance-Partial Mutual Information Clustering With Least Square Regression

Content Provider	IEEE Xplore Digital Library
Author	Chao Chen Xuefeng Yan
Copyright Year	2012
Abstract	In this paper, an optimized multilayer feed-forward network (MLFN) is developed to construct a soft sensor for controlling naphtha dry point. To overcome the two main flaws in the structure and weight of MLFNs, which are trained by a back-propagation learning algorithm, minimal redundancy maximal relevance-partial mutual information clustering (mPMIc) integrated with least square regression (LSR) is proposed to optimize the MLFN. The mPMIc can determine the location of hidden layer nodes using information in the hidden and output layers, as well as remove redundant hidden layer nodes. These selected nodes are highly related to output data, but are minimally correlated with other hidden layer nodes. The weights between the selected hidden layer nodes and output layer are then updated through LSR. When the redundant nodes from the hidden layer are removed, the ideal MLFN structure can be obtained according to the test error results. In actual applications, the naphtha dry point must be controlled accurately because it strongly affects the production yield and the stability of subsequent operational processes. The mPMIc-LSR MLFN with a simple network size performs better than other improved MLFN variants and existing efficient models.
Page Count	11
File Size	2439826
Starting Page	1177
Ending Page	1187
File Format	PDF
ISSN	2162237X
Volume Number	26
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Redundancy Artificial neural networks Training Kernel Nonhomogeneous media Vectors partial mutual information (PMI). Least square regression (LSR) minimal redundancy maximal relevance multilayer feed-forward network (MLFN) naphtha dry point partial mutual information (PMI)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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