U.S. Banu∗ and G. Uma∗∗
[1] J. Wang, A.B. Rad, & P.T. Chan, Indirect adaptive fuzzysliding model control; part 1: Fuzzy switching, Fuzzy Sets andSystems, 122, 2001, 21–30. [2] C.-T. Chen & S.-T. Peng, Intelligent process control usingneural fuzzy techniques, Journal of Process Control, 9, 1999,493–503. [3] B.W. Bequette, Nonlinear control of chemical process: a review,Industrial & Engineering Chemistry Research, 30, 1991, 1391–1398. [4] D.E. Seborg, A perspective on advanced strategies for processcontrol, modeling, Identification and Control, 15, 1994, 179–189. [5] G. Stephanopoulos & C. Han, Intelligent systems in processengineering: a review, Computers & Chemical Engineering,20, 1996, 743–791. [6] K.J. Hunt, D. Sbarbaro, R. Zbikowski, & P.J. Gawthrop,Neural networks for control systems – a survey. Automatica,28(6), 1992, 1083–1112. [7] D. Xiaosong, D. Popovic, & G. Schulz-Ekloff, Real timeidentification and control of a continuous stirred tank reactorwith neural network, IEEE Transactions, 1995, 67–70. [8] K.S. Narendra & K. Parthasarathy, Identification and control ofdynamical systems using neural networks, IEEE Transactionson Neural Networks, 1(1), 4–27. [9] R. Hecht-Nielsen, Neurocomputing, (NY: Addison Wesley Pub-lishing Company, 1990). [10] T. Takagi & M. Sugeno, Fuzzy identification of systems and itsapplications to modeling and control, IEEE Transactions onSystem, Man and Cybernetics, SMC-15(1), January/February1985, 116–132. [11] E.H. Mamdani & P.J. King, The application of fuzzy controlsystems to industrial processes, Automatica, 13, 1997, 235–242. [12] C. Sheng, J. Jingping, & Y. Huigeneg, The nonlinear adaptivecontrol of CSTR, Information and Control, April 1992, 124–128. [13] Z. X. Dai & G. Xie. A new optimization search algorithm-GA,The Control Theory & Application, June 1995, 225–272. [14] W. Zho. Multivariable adaptive control for a space stationusing genetic algorithm, IEE Proceedings – Control Theoryand Applications, 142(2), March 1995, 81–87. [15] J.C. Hoskins & D.M. Himmelblau, Artificial neural networkmodels of knowledge representation in chemical engineering,Computers & Chemical Engineering, 12(9/10), 1988, 881–890.363 [16] M. Morari & E. Zafiriou, Robust process control (EnglewoodCliffs, USA: Prentice Hall, 1989). [17] J.M. Zurada, Introduction to artificial neural systems (Mumbai,Jaico Publishing House: 1994). [18] C. Johnson, Process control instrumentation technology (NewDelhi, Prentice Hall, India: 1999). [19] G. Stephanopoulas, Chemical process control (New Delhi,Prentice Hall, India: 1995).
Important Links:
Go Back