Recurrent Neural Network Based Fuzzy Inference System for Identification and Control of Dynamic Plants
This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The neuro-fuzzy system is used for the identification and control of nonlinear dynamic plant. The simulation results of identification and control systems based on recurrent neuro-fuzzy network are compared with the simulation results of other neural systems. It is found that the recurrent neuro-fuzzy based system has better performance than the others.
[1] Zadeh L.A.(1975). The concept of linguistic variable and its
application to approximate reasoning. Information Sciences, v.8.
[2] Kosko B. (1993). Neural networks and fuzzy systems. A
dynamical system approach to machine intelligence. Prentice-
Hall International Inc., Englewood Cliffs.
[3] Yager R.R., Zadeh L.A.(Eds). (1994). Fuzzy sets, neural
networks and softcomputing, New York, Van Nostrand
Reinhold.
[4] Witold Pedryz, editor, (1996) Fuzzy Modelling: Paradigms and
Practice, Kluwer Academic Publisher, Boston.
[5] Aliev R.A., Tserkovni A.E., and Mamedova G.A. (1991).
Production management at fuzzy initial information. Moscow,
Energiatomizdat, (Russian)
[6] J.Reeman and D.Saad.(1997). Online learning in radial basis
function networks, Neural Comput., vol.9,no.7,
[7] T.Mheskes and B. Kappen. (1993). Online learning processes in
artificial neural networks. Math. Found. Neural Networks,
Amsterdam, The Netherlands: Elsevier, pp.199-233.
[8] Diederich J. (1990). Artificial Neural Networks. Consept
learning, Los Alamitos CA: IEEE Computer Society Press.
[9] Jyh-Shing Roger Jang. (1993). "ANFIS: Adaptive-Network-
Based Fuzzy Inference System". IEEE Transactions on Systems,
Man and Cybernetics, Vol.23, No.3, pp.665-683.
[10] Nauck, Detlef and Kruse, Rudolf. (1996). Designing neurofuzzy
systems through backpropagation, In Witold Pedryz,
editor, Fuzzy Modelling: Paradigms and Practice, Boston,
Kluwer Academic Publisher,pp.203-228
[11] Detlef Nauck. (1994). Building neural-fuzzy controllers with
NEFCON-I. In Rudolf Kruse, Jorg Gebhardt, and Rainer
Palm(Eds), Fuzzy Systems in Computer Science, Artificial
Intelligence, Wiesbaden, Vieweg,pp.141-151.
[12] M.Onder Efe, and Okyay Kaynak. (2000). "On stabilization of
Gradient-Based Training Strategies for Computationally
Intelligent Systems". IEEE Transactions on Fuzzy Systems,
Vol.8, No.5, October, pp.564-575.
[13] J.J.Buckley, Y.Hayashi, and E.Czogola.(1993). Fuzzy neural
networks with fuzzy signals and weights. International Journal
on Intelligent Systems 8, pp.527-537.
[14] J.J.Buckley, and Y.Hayashi. (1993). Fuzzy neural networks. In
L.A.Zadeh and R.R.Yager (Eds), Fuzzy Sets, Neural networks
and Soft Computing, Van Nostrand Reinhold, pp.233-249.
[15] H.Ishibuchi, K. Morioka, and H.Tanaka. (1994). A fuzzy neural
network with trapezoidal fuzzy weights. Proc. FUZZ-IEEE,
Orlando, Florida, June 26-29, pp.228-233.
[16] R.A.Aliev, R.H.Abiyev, and R.R.Aliev. (1994). Automatic
control system synthesis with the learned neural network based
fuzzy controller. Moscow, News of Academy of Sciences,
Tech. Cybernetics 2:pp. 192-197.
[17] R.A.Aliev, F.T.Aliev, R.H.Abiev, and R.R.Aliev. (1994).
Industrial neural controllers. EUFIT-94, Promenade 9,52076,
Aachen, Germany. Elita foundation
[18] R.H.Abiyev, K.W.Bonfig, and F.T.Aliev. (1996). Controller
based on fuzzy neural network for control of technological
process. ICAFS-96, Siegen, Germany, June 25-27,pp295-298.
[19] J.Zhang, and A.J.Morris, (1999). Recurrent neuro-fuzzy
networks for nonlinear process modeling. IEEE Trans. Neural
Networks, vol.10,no.2, Mart, pp.313-326,
[20] C.H.Lee, and C.C.Theng. (2000). Identification and control of
dynamic systems using recurrent fuzzy neural network. IEEE
Trans. Fuzzy Systems, vol. 8, pp.349-366.
[21] Chia-Feng Juang. (2002), A TSK -type recurrent fuzzy network
for dynamic systems processing by neural network and genetic
algorithm, IEEE Trans. Fuzzy Systems, vol.10, pp.155-170.
[22] James Keller, Ronald R.Yager, and Hossein Tahani. (1992).
Neural network implementation of fuzzy logic, Fuzzy Sets and
Systems, 45:pp.1-12.
[23] Rahib Abiyev. (2001). Controllers based on Softcomputing
elements// Electrical, Electronics and Computer Engineering
Symposium NEU-CEE2001 & Exhibition. Nicosia, TRNC,
Turkey, May 23-25, pp.182-188.
[24] Rahib Abiyev. (2002). Fuzzy inference system based on neural
network for technological processes control. Journal of
Mathematical and Computational Applications. Turkey, pp.245-
252.
[25] Jaier Nunez-Garcia and Olaf Wolkenhauer. (2002). Random Set
System Identification. IEEE Transactions on Fuzzy Systems,
Vol.10, No.3, October, pp.287-296.
[26] Rahib Abiyev. (2002). Neuro-Fuzzy system for technological
processes control. The 6th World Multi-Conference on
SYSTEMICS, cybernetics and informatics. SCI-20002, Orlando,
Florida, USA. July 14-18.
[1] Zadeh L.A.(1975). The concept of linguistic variable and its
application to approximate reasoning. Information Sciences, v.8.
[2] Kosko B. (1993). Neural networks and fuzzy systems. A
dynamical system approach to machine intelligence. Prentice-
Hall International Inc., Englewood Cliffs.
[3] Yager R.R., Zadeh L.A.(Eds). (1994). Fuzzy sets, neural
networks and softcomputing, New York, Van Nostrand
Reinhold.
[4] Witold Pedryz, editor, (1996) Fuzzy Modelling: Paradigms and
Practice, Kluwer Academic Publisher, Boston.
[5] Aliev R.A., Tserkovni A.E., and Mamedova G.A. (1991).
Production management at fuzzy initial information. Moscow,
Energiatomizdat, (Russian)
[6] J.Reeman and D.Saad.(1997). Online learning in radial basis
function networks, Neural Comput., vol.9,no.7,
[7] T.Mheskes and B. Kappen. (1993). Online learning processes in
artificial neural networks. Math. Found. Neural Networks,
Amsterdam, The Netherlands: Elsevier, pp.199-233.
[8] Diederich J. (1990). Artificial Neural Networks. Consept
learning, Los Alamitos CA: IEEE Computer Society Press.
[9] Jyh-Shing Roger Jang. (1993). "ANFIS: Adaptive-Network-
Based Fuzzy Inference System". IEEE Transactions on Systems,
Man and Cybernetics, Vol.23, No.3, pp.665-683.
[10] Nauck, Detlef and Kruse, Rudolf. (1996). Designing neurofuzzy
systems through backpropagation, In Witold Pedryz,
editor, Fuzzy Modelling: Paradigms and Practice, Boston,
Kluwer Academic Publisher,pp.203-228
[11] Detlef Nauck. (1994). Building neural-fuzzy controllers with
NEFCON-I. In Rudolf Kruse, Jorg Gebhardt, and Rainer
Palm(Eds), Fuzzy Systems in Computer Science, Artificial
Intelligence, Wiesbaden, Vieweg,pp.141-151.
[12] M.Onder Efe, and Okyay Kaynak. (2000). "On stabilization of
Gradient-Based Training Strategies for Computationally
Intelligent Systems". IEEE Transactions on Fuzzy Systems,
Vol.8, No.5, October, pp.564-575.
[13] J.J.Buckley, Y.Hayashi, and E.Czogola.(1993). Fuzzy neural
networks with fuzzy signals and weights. International Journal
on Intelligent Systems 8, pp.527-537.
[14] J.J.Buckley, and Y.Hayashi. (1993). Fuzzy neural networks. In
L.A.Zadeh and R.R.Yager (Eds), Fuzzy Sets, Neural networks
and Soft Computing, Van Nostrand Reinhold, pp.233-249.
[15] H.Ishibuchi, K. Morioka, and H.Tanaka. (1994). A fuzzy neural
network with trapezoidal fuzzy weights. Proc. FUZZ-IEEE,
Orlando, Florida, June 26-29, pp.228-233.
[16] R.A.Aliev, R.H.Abiyev, and R.R.Aliev. (1994). Automatic
control system synthesis with the learned neural network based
fuzzy controller. Moscow, News of Academy of Sciences,
Tech. Cybernetics 2:pp. 192-197.
[17] R.A.Aliev, F.T.Aliev, R.H.Abiev, and R.R.Aliev. (1994).
Industrial neural controllers. EUFIT-94, Promenade 9,52076,
Aachen, Germany. Elita foundation
[18] R.H.Abiyev, K.W.Bonfig, and F.T.Aliev. (1996). Controller
based on fuzzy neural network for control of technological
process. ICAFS-96, Siegen, Germany, June 25-27,pp295-298.
[19] J.Zhang, and A.J.Morris, (1999). Recurrent neuro-fuzzy
networks for nonlinear process modeling. IEEE Trans. Neural
Networks, vol.10,no.2, Mart, pp.313-326,
[20] C.H.Lee, and C.C.Theng. (2000). Identification and control of
dynamic systems using recurrent fuzzy neural network. IEEE
Trans. Fuzzy Systems, vol. 8, pp.349-366.
[21] Chia-Feng Juang. (2002), A TSK -type recurrent fuzzy network
for dynamic systems processing by neural network and genetic
algorithm, IEEE Trans. Fuzzy Systems, vol.10, pp.155-170.
[22] James Keller, Ronald R.Yager, and Hossein Tahani. (1992).
Neural network implementation of fuzzy logic, Fuzzy Sets and
Systems, 45:pp.1-12.
[23] Rahib Abiyev. (2001). Controllers based on Softcomputing
elements// Electrical, Electronics and Computer Engineering
Symposium NEU-CEE2001 & Exhibition. Nicosia, TRNC,
Turkey, May 23-25, pp.182-188.
[24] Rahib Abiyev. (2002). Fuzzy inference system based on neural
network for technological processes control. Journal of
Mathematical and Computational Applications. Turkey, pp.245-
252.
[25] Jaier Nunez-Garcia and Olaf Wolkenhauer. (2002). Random Set
System Identification. IEEE Transactions on Fuzzy Systems,
Vol.10, No.3, October, pp.287-296.
[26] Rahib Abiyev. (2002). Neuro-Fuzzy system for technological
processes control. The 6th World Multi-Conference on
SYSTEMICS, cybernetics and informatics. SCI-20002, Orlando,
Florida, USA. July 14-18.
@article{"International Journal of Information, Control and Computer Sciences:64757", author = "Rahib Hidayat Abiyev", title = "Recurrent Neural Network Based Fuzzy Inference System for Identification and Control of Dynamic Plants", abstract = "This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The neuro-fuzzy system is used for the identification and control of nonlinear dynamic plant. The simulation results of identification and control systems based on recurrent neuro-fuzzy network are compared with the simulation results of other neural systems. It is found that the recurrent neuro-fuzzy based system has better performance than the others.
", keywords = "Fuzzy logic, neural network, neuro-fuzzy system, control system.", volume = "1", number = "7", pages = "2275-10", }
