Identification of MIMO Systems Using Neuro-Fuzzy Models with a Shuffled Frog Leaping Algorithm

Authors: Sana Bouzaida, Anis Sakly, Faouzi M'Sahli

Abstract:

In this paper, a TSK-type Neuro-fuzzy Inference System that combines the features of fuzzy sets and neural networks has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on Particle Swarm Optimization algorithm (PSO) and Genetic Algorithm (GA).

Keywords: Identification, Shuffled frog Leaping Algorithm (SFLA), TSK-type neuro-fuzzy model.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062398

References:

[1] Y. Zhou, S. Li, R. Jin, "A new fuzzy neural network with fast learning algorithm and guaranteed stability for manufacturing process control", Fuzzy Sets Syst, pp 201-216, December 2002.
[2] C.-C. Chuang, S.-F. Su, S.-S. Chen, "Robust TSK fuzzy modeling for function approximation with outliers", IEEE Transactions on Fuzzy Systems, pp 810-821, 2001.
[3] J. Kennedy, RC. Eberhart, "Particle Swarm Optimization", Proceedings of the IEEE International Conference on Neural Networks, pp. 1942- 1948, 1995.
[4] G. Leng, T.M. McGinnity, G. Prasad, Design for self-organizing fuzzy neural networks based on genetic algorithms, IEEE Transactions on Fuzzy Systems, pp 755-766, 2006.
[5] M. Eusuff, K. Lansey, and F. Pasha, Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization, Engineering Optimization, pp 12-154, 2006.
[6] T.-H. Huynh, A modified shullfed frog leaping algorithm for optimal tuning of multivariable PID controllers, IEEE International Conference on Industrial Technology, 2008, pp 20-23.
[7] Eslamian, M., S.H. Hosseinian, and B. Vahidi, Bacterial Foraging-Based Solution to the Unit-Commitment Problem, IEEE Transactions on Power Systems, pp 1478 -1488, 2009.
[8] A. Rahimi-Vahed and A. H. Mirzaei, Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm, Soft Computing, pp 435-52, 2008.
[9] S. Purwar, I.N. Kar, A.N. Jha, On-line system identification of complex systems using Chebyshev neural networks, Applied Soft Computing 7, pp. 364-372, 2007.
[10] K.S. Narendra, Identification and control of dynamical systems using neural network, IEEE Transactions on Neural Networks, pp 4-27, 1990.