Adaptive Neuro-Fuzzy Model with Fuzzy Clustering for Nonlinear Prediction and Control

Bayadir Abbas Al-Himyari, Azman Yasin, Horizon Gitano

Abstract


Nonlinear systems have more complex manner and profoundness than linear systems. Thus, their analyses are much more difficult. This paper presents the use of neuro-fuzzy networks as means of implementing algorithms suitable for nonlinear black-box prediction and control. In engineering applications, two attractive tools have emerged recently. These two attractive tools are: the artificial neural networks and the fuzzy logic system. One area of particular importance is the design of networks capable of modeling and predicting the behavior of systems that involve complex, multi-variable processes. To illustrate the applicability of the neuro-fuzzy networks, a case study involving air-fuel ratio is presented here. Air-fuel ratio represents complex, nonlinear and stochastic behavior. To monitor the engine conditions, an adaptive neuro-fuzzy inference system (ANFIS) is used to capture the nonlinear connections between the air-fuel ratio and control parameters such manifold air pressure, throttle position, manifold air temperature, engine temperature, engine speed, and injection opening time. This paper describes a fuzzy clustering method to initialize the ANFIS.


Keywords


ANFIS, Fuzzy Clustering, Air-fuel ratio

Full Text:

PDF

References


A. R. Sadeghian, “Nonlinear Neuro-Fuzzy Prediction: Methodology, Design and Applications”, 2001.

C.J. Harris, M. Brown, K.M. Bossley, D.J. Mills, F. Ming,”Advances in Neurofuzzy Algorithms for Real-time Modelling

and Control”, Engineering Applications of Artificial Intelligence, Vol. 9, Issue 1, pp. 1-16, February 1996.

E. Gorrostiet, J.C. Pedraza, R.J. Carlos,”Fuzzy Modelling of Systems”, Proceedings of 11 th IEEE International

Conference on Methods and Models in Automation and Robotics MMAR 2005, 29 August- 1 September 2005,

Miedzyzdroje, Poland. ISBN 83-60140-85-5.

H. O. Wang, K. Tanaka, and M. F. Griffin, “An approach to fuzzy control of nonlinear systems: stability and design

issues”, IEEE Trans. on Fuzzy Systems, vol. 4, no. 1, pp. 14-23, Feburary 1996.

J.C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.

J. Jang, “ANFIS: Adaptive network-based fuzzy inference systems”, IEEE Transactions on Systems, Man, and

Cybernetics 23, pp.665-685, 1993.

J. Lauber, T. M. Guerra, M. Dambrine,” Air-fuel ratio control in a gasoline engine”. International Journal of Systems

Science, Vol. 42, No. 2, pp. 277-286, 2011.

K.P. Mohanadas and S. Karimulla,“Fuzzy and neuro-fuzzy modelling and control of nonlinear systems”.

L.A. Zadeh, “Fuzzy sets”, Information Control 8 pp.338–353, 1965.

L. Hong-Xing, C. L. Phillip Chen, “The equivalence Between Fuzzy Logic and Feedforward Neural Networks”, IEEE

Trans. On Neural Networks, vol. 11, no. 2, March 2000.

M.S. Yang, C.H. Ko, “On a class of fuzzy c-numbers clustering procedures for fuzzy data”, Fuzzy Sets and Systems 84,

–60, 1996.

Robert fuller, Introduction to Neuro-Fuzzy Systems, springer, 2000.


Refbacks

  • There are currently no refbacks.