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A genetic based neuro-fuzzy controller for thermal processes
Keywords:
Neuro-fuzzy controller, fuzzy logic, modelling, neural networks, genetic algorithmsAbstract
This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori knowledge about the system and eliminates the need for complicated design steps like manual tuning of input-output membership functions, and selection of fuzzy rule base. Although, only conventional genetic algorithms have been used, convergence results are very encouraging. A well known numerical example derived from literature is used to evaluate and compare the performance of the network with other modelling approaches. The network is further implemented as controller for two simulated thermal processes and their performances are compared with other existing controllers. Simulation results show that the proposed neuro-fuzzy controller whose all parameters have been tuned simultaneously using GAs, offers advantages over existing controllers and has improved performance.
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References
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[2] J. R .Jang, “Self-learning Fuzzy Controllers Based on Temporal Backpropagation”, IEEE Transactions on Neural Networks, Vol. 3, 1992, pp. 714-721.
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[5] W. A. Farag, V. H. Quintana, and G. LambertTorres, “A Genetic-Based Neuro-Fuzzy Approach for Modelling and Control of Dynamical Systems”, IEEE Transactions on Neural Networks, Vol. 9, No. 5, 1998, pp. 756-767.
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[8] T. L. Seng, M. B. Khalid, and R. Yusof, “Tuning of a Neuro-Fuzzy Controller by Genetic Algorithm”, IEEE Transactions on Systems, Man & Cybernetics, Vol. 29, 1999, pp. 226-236.
[9] K. S. Narendra and K. Parthasarathy, “Identification and Control of Dynamical Systems Using Neural Networks”, IEEE Transactions on Neural Networks, Vol. 1, No. 1, 1990, pp. 4-27.
[10] C. T. Lin and C. S. G. Lee, “Neural Network Based Fuzzy Logic Control and Decision System”, IEEE Transactions on Computer., Vol. 40, 1991, pp. 1320-1336.
[11] A. Homaifar and E. Mccormick, “Simultaneous Design of Membership Functions and Rule Sets for Fuzzy Controllers Using Genetic Algorithms”, IEEE Transactions on Fuzzy Systems, Vol. 3, 1995.
[12] T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modelling and Control”, IEEE Transactions on Systems, Man & Cybernetics, Vol. 15, 1985.
[13] L. Wang and R. Langari, “Complex Systems Modelling via Fuzzy Logic”, IEEE Transactions on Systems, Man & Cybernetics, Vol. 26, 1996, pp. 100-106.
[14]Han-Xiong Li and H. B. Gatland, “A New Methodology for Designing a Fuzzy Logic Controller”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 25, No. 3, 1995, pp. 505-512.
[15] M. Khalid, S. Omato and R. Yusof, “Temperature Regulation with Neural Networks and Alternative Control Schemes”, IEEE Transactions on Neural Networks, Vol. 6, No. 3, 1995, pp. 572-582.
[16] B. W. Bequette, Process Dynamics –Modelling, Analysis and Simulation, Prentice Hall PTR, 1998.
[17] M. Hassoun, Fundamentals of Artificial Neural Networks, Prentice Hall India, 1998.
[2] J. R .Jang, “Self-learning Fuzzy Controllers Based on Temporal Backpropagation”, IEEE Transactions on Neural Networks, Vol. 3, 1992, pp. 714-721.
[3] C. L. Karr and E. J. Gentry, “Fuzzy Control of pH Using Genetic Algorithms”, IEEE Transactions on Fuzzy Systems, Vol. 1, 1993, pp. 46-53.
[4] A. Varsek, T. Urbancic, and B. Filipic, “Genetic Algorithms in Controller Design and Tuning”, IEEE Transactions on Systems, Man & Cybernetics, Vol. 23, 1993.
[5] W. A. Farag, V. H. Quintana, and G. LambertTorres, “A Genetic-Based Neuro-Fuzzy Approach for Modelling and Control of Dynamical Systems”, IEEE Transactions on Neural Networks, Vol. 9, No. 5, 1998, pp. 756-767.
[6] M. Lee and H. Takagi, “Integrated Design stages of Fuzzy Systems Using Genetic Algorithms”, in Proc. FUZZ-IEEE’93, San Fancisco CA, 1993, pp. 612-617.
[7] K. Shimojima, T. Fukuda, and Y. Hasegawa, “Self-tuning Fuzzy Modelling with Adaptive Membership Function, Rules, and Hierarchical Structure Based on Genetic Algorithm”, Fuzzy Sets and Systems, Vol. 71, 1995, pp. 295-309.
[8] T. L. Seng, M. B. Khalid, and R. Yusof, “Tuning of a Neuro-Fuzzy Controller by Genetic Algorithm”, IEEE Transactions on Systems, Man & Cybernetics, Vol. 29, 1999, pp. 226-236.
[9] K. S. Narendra and K. Parthasarathy, “Identification and Control of Dynamical Systems Using Neural Networks”, IEEE Transactions on Neural Networks, Vol. 1, No. 1, 1990, pp. 4-27.
[10] C. T. Lin and C. S. G. Lee, “Neural Network Based Fuzzy Logic Control and Decision System”, IEEE Transactions on Computer., Vol. 40, 1991, pp. 1320-1336.
[11] A. Homaifar and E. Mccormick, “Simultaneous Design of Membership Functions and Rule Sets for Fuzzy Controllers Using Genetic Algorithms”, IEEE Transactions on Fuzzy Systems, Vol. 3, 1995.
[12] T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modelling and Control”, IEEE Transactions on Systems, Man & Cybernetics, Vol. 15, 1985.
[13] L. Wang and R. Langari, “Complex Systems Modelling via Fuzzy Logic”, IEEE Transactions on Systems, Man & Cybernetics, Vol. 26, 1996, pp. 100-106.
[14]Han-Xiong Li and H. B. Gatland, “A New Methodology for Designing a Fuzzy Logic Controller”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 25, No. 3, 1995, pp. 505-512.
[15] M. Khalid, S. Omato and R. Yusof, “Temperature Regulation with Neural Networks and Alternative Control Schemes”, IEEE Transactions on Neural Networks, Vol. 6, No. 3, 1995, pp. 572-582.
[16] B. W. Bequette, Process Dynamics –Modelling, Analysis and Simulation, Prentice Hall PTR, 1998.
[17] M. Hassoun, Fundamentals of Artificial Neural Networks, Prentice Hall India, 1998.
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Published
2005-04-01
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[1]
“A genetic based neuro-fuzzy controller for thermal processes”, JCS&T, vol. 5, no. 01, pp. p. 37–43, Apr. 2005, Accessed: Jan. 14, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/886
