Thermal System Modeling Based on Entropy and BP Neural Network

doi:10.16182/j.issn1004731x.joss.201701030

Abstract

Abstract: The real-time modeling is difficult on thermal system and the model precision is not high. The convergence rate of Neural Network (NN) decreases dramatically when there are too many inputs. The BP NN modeling method based on information entropy was proposed in which the attribute reduction based on the model of approximation decision entropy was used. Field data were preprocessed by the k-means clustering algorithm and the validity was then evaluated. The approximation decision entropy was used in attribute reduction of the inputs. And the nonlinear model was built by the training of BP NN. Eventually, the proposed method was applied to the modeling of main steam temperature and the modeling of NOx emission concentration and the result shows that, the method simplified the structure of network and increased the training speed with high-precision of the model. The method is of great significance on real-time modeling and has higher practical value.

Key words: rough set, attribute reduction, approximation decision entropy, NN, nonlinear modeling

CLC Number:

TP391

Sun Hairong, Wang Rui, Geng Junya. Thermal System Modeling Based on Entropy and BP Neural Network[J]. Journal of System Simulation, 2017, 29(1): 226-233.

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