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CLC number: TP273.4

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Received: 2000-11-07

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Bio-Design and Manufacturing  2018 Vol.3 No.1 P.57~59

10.1631/jzus.2002.0057


Fuzzy NN based predictive control and its application to green liquor system


Author(s):  LI Jiang, ZHOU Wei, ZHANG Liang-jun, LI Ping

Affiliation(s):  Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s): 

Key Words:  model predictive control, fuzzy neural networks, generalized predictive control


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LI Jiang, ZHOU Wei, ZHANG Liang-jun, LI Ping. Fuzzy NN based predictive control and its application to green liquor system[J]. Journal of Zhejiang University Science D, 2018, 3(1): 57~59.

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Abstract: 
The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC algorithm is used as the predictive component. The fuzzy neural network has six layers, including input layer, output layer and four hidden layers. An application to a MIMO nonlinear process(green liquor system of the recovery system in a pulp factory shows that this algorithm has better performance than normal PID algrithm.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Clarke,D.W., Mohtadi,C., Tuffs,P.S., 1987. Generalized Predictive Control, Part I and Part II. Automatica, 23(2):137-160.

[2] Roger Jang,J.S., 1993. ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Trans. on Systems, Man and Cybernetics, 23(3):665-685.

[3] Sousa,J.M., Babuka,R., Verbruggen,H.B., 1997. Fuzzy predictive control applied to an air-conditioning system. Control Engineering Practice, 5(10):1395-1406.

[4] Wang,S.Y., Yang,D.Q., Liu,G.H., et al., 1991. Optimization Concepts, Methods and Applications. Zhejiang University Press, Hangzhou, p.317-319.

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