IEEE Trans Neural Netw Learn Syst - Optimization of a multilayer neural network by using minimal redundancy maximal relevance-partial mutual information clustering with least square regression.

Tópicos

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Resumo

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.

Resumo Limpo

paper optim multilay feedforward network mlfn develop construct soft sensor control naphtha dri point overcom two main flaw structur weight mlfns train backpropag learn algorithm minim redund maxim relevanceparti mutual inform cluster mpmic integr least squar regress lsr propos optim mlfn mpmic can determin locat hidden layer node use inform hidden output layer well remov redund hidden layer node select node high relat output data minim correl hidden layer node weight select hidden layer node output layer updat lsr redund node hidden layer remov ideal mlfn structur can obtain accord test error result actual applic naphtha dri point must control accur strong affect product yield stabil subsequ oper process mpmiclsr mlfn simpl network size perform better improv mlfn variant exist effici model

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