NDLI: Model reference neural network control for boiler combustion system

Content Provider	IEEE Xplore Digital Library
Author	Xiu-Cheng Dong Hai-Bin Wang Xiao-Xiao Zhao
Copyright Year	2005
Description	Author affiliation: Sch. of Electr. & Inf. Eng., Xihua Univ., Sichuan, China (Xiu-Cheng Dong; Hai-Bin Wang; Xiao-Xiao Zhao)
Abstract	It is difficult to have good performance for chain boiler combustion control system due to large delay time, varying coal's quality and steam load. A neural network identification method for nonlinear system's delay time is discussed. Using the abrupt mutation resulted from the training error sum square of the real output and the expected output of the network, this method changes the input sample period of the neural network so that it can discriminate the delay time of the nonlinear model. Combining the discrimination of neural network system with long time delay and the control method based on model reference, it can be applied to control the nonlinear long delay time system with variable parameters or unknown delay time. Simulating with a 10t/h chain boiler model, the results show it has much better advantage of celerity and robustness.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	4694
Ending Page	4698
File Size	328526
Page Count	5
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527767
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Boilers Combustion Delay effects Delay systems Control systems Delay lines Process control Equations Nonlinear control systems reference model Neural network controller chain boiler delay time system identification of delay time
Content Type	Text
Resource Type	Article

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