Identification of a Nonlinear System by Determining of Fuzzy Rules
الموضوعات :
hojatallah hamidi
1
(Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran)
Atefeh Daraei
2
(K. N.Toosi University of Technology)
الکلمات المفتاحية: Data mining , Classification , Heart disease , Diagnosis , Prognosis , Treatment,
ملخص المقالة :
In this article the hybrid optimization algorithm of differential evolution and particle swarm is introduced for designing the fuzzy rule base of a fuzzy controller. For a specific number of rules, a hybrid algorithm for optimizing all open parameters was used to reach maximum accuracy in training. The considered hybrid computational approach includes: opposition-based differential evolution algorithm and particle swarm optimization algorithm. To train a fuzzy system hich is employed for identification of a nonlinear system, the results show that the proposed hybrid algorithm approach demonstrates a better identification accuracy compared to other educational approaches in identification of the nonlinear system model. The example used in this article is the Mackey-Glass Chaotic System on which the proposed method is finally applied.
[1] S. Chen, S.A. Billings, Representation of non-linear systems: the NARMAX model, Int. J. Control 49 (1989) 1013–1032.#
[2] H. Hujiberts, H. Nijmeijer, R. Willems, System identification in communication with chaotic systems, IEEE Trans. Circuits Syst. I 47 (6), 2000, pp. 800–808.#
[3] M. Adjrad, A. Belouchrani, Estimation of multi component polynomial phase signals impinging on a multisensor array using state-space modeling, IEEE Trans. Signal Process. 55 (1), 2007.pp. 32–45.#
[4] K. Watanbe, I. Matsuura, M. Abe, M. Kebota, D.M. Himelblau, Incipient fault diagnosis of chemical processes via artificial neural networks, AICHE J. 35 (11) (1989) 1803–1812.#
[5] Y. Xie, B. Guo, L. Xu, J. Li, P. Stoica, Multistatic adaptive microwave imaging for early breast cancer detection, IEEE Trans. Biomed. Eng. 53 (8), 2006, pp. 1647–1657.#
[6] D. Chen, J. Wang, F. Zou, H. Zhang, W. Hou, “Linguistic fuzzy model identification based on PSO with different length of particles”, Applied Soft Computing 12, pp. 3390–3400, 2012.#
[7] C. F. Juang, and P. H. Chang, “Designing Fuzzy-Rule-Based Systems Using Continuous Ant-Colony Optimization”, IEEE Trans on Fuzzy Syst., vol. 18, no. 1, pp. 138-149, Feb 2010.#
[8] C. F. Juang, C. Lo, “Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm”, Fuzzy Sets and Systems 159, 2910 – 2926, 2008.#
[9] R. Storn, System design by constraint adaptation and differential evolution, IEEE Trans. Evol. Comput. 3 (1999) 22–34.#
[10] J. Ilonen, J.K. Kamarainen, J. Lampinen, Differential evolution training algorithm for feed forward neural networks, Neural Proc. Lett. 17 (2003) 93–105.#
[11] E. Goldberg, J. Richardson, Genetic algorithms with sharing for multimodal function optimization, in: J. Richardson (Ed.), Genetic Algorithms and their Applications (ICGA’87)., 1987, pp. 41–49.#
[12] K. Kristinsson, G.A. Dumont, System identification and control using genetic algorithms, IEEE Trans. Syst. Man Cybernet. 22 (1992) 1033–1046.#
[13] J. Ilonen, J.K. Kamarainen, J. Lampinen, Differential evolution training algorithm for feed forward neural networks, Neural Proc. Lett. 17, 2003, pp.93–105.#
[14] R. Storn, System design by constraint adaptation and differential evolution, IEEE Trans. Evol. Comput. 3, 1999, pp. 22–34.#
[15] H.R. Tizhoosh, Opposition-based learning: a new scheme for machine intelligence, in: Proc. Int. Conf. Comput. Intell. Modeling Control and Autom, vol. I, Vienna, Austria, 2005, pp. 695–701.
[16] S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama, Opposition Versus Randomness in Soft Computing Techniques, Elsevier J. Appl. Soft Comput. 8 (March (2)), 2008, pp. 906–918.#
[17] S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama, Opposition-based differential evolution, IEEE Trans. Evol. Comput. 12 (1), 2008.#
[18] K. V. Price, R. M. Storn, and J. A. Lampinen, "Differential Evolution: A Practical Approach to Global Optimization", (Kindle Edition). Springer, 2005.#
[19] H.R. Tizhoosh, Opposition-based learning: a new scheme for machine intelligence, in: Proc. Int. Conf. Comput. Intell. Modeling Control and Autom, vol. I, Vienna, Austria, 2005, pp. 695–701.#
[20] J. Kennedy, R. Eberhart, “Particle swarm optimization”, in: Proc. IEEE Internat. Conf. Neural Networks, Perth, Australia, pp. 1942–1948, 1995.#
[21] C. F. Juang, C. W. Hung and C. H. Hsu, “Rule-Based Cooperative Continuous Ant Colony Optimization to Improve the Accuracy of Fuzzy System Design”, IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 22, NO. 4, AUGUST 2014.#
[22] W. Zhao, Q. Niu, K. Li and G. W. Irwin, “A Hybrid Learning Method for Constructing Compact Rule-Based Fuzzy Models”, IEEE TRANSACTIONS ON CYBERNETICS, VOL. 43, NO. 6, DECEMBER 2013. #
[23] H.Hamidi, “A Model for Impact of Organizational Project Benefits Management and its Impact on End User”, JOEUC, Volume 29, Issue 1, 2017, pp.50-64.#
[24] K.Mohammadi, H.Hamidi., Modeling and Evolution of Fault-Tolerant Mobile Agents in Distributed System.The Second IEEE and IFIP International Conference on wireless and Optical Communications Networks (WOCN 2005), March 6 –8, 2005.#
[25] S. A.Monadjemi, H.Hamidi, A.Vafaei. Analysis and Evaluation of a New Algorithm Based Fault Tolerance for Computing Systems. International Journal of Grid and High Performance Computing (IJGHPC), 4(1), 2012, pp. 37-51.
[26] S. A.Monadjemi, H..Hamidi., A.Vafaei. “ANALYSIS AND DESIGN OF AN ABFT AND PARITY-CHECKING TECHNIQUE IN HIGH PERFORMANCE COMPUTING SYSTEMS” Journal of Circuits, Systems, and Computers (JCSC), JCSC Volume 21 Number 3, 2012.#
[27] A.Vafaei., S. A.Monadjemi, H..Hamidi., Evaluation of Fault Tolerant Mobile Agents in Distributed Systems. International Journal of Intelligent Information Technologies (IJIIT), 5(1), 2009, pp.43-60. #
[28] A.Vafaei, S. A.Monadjemi, H.Hamidi “Evaluation and Check pointing of Fault Tolerant Mobile Agents Execution in Distributed Systems,” Journal of Networks, VOL. 5, NO. 7. 2009. #
[29] H.Hamidi, A New Method for Transformation Techniques in Secure Information Systems Journal of Information Systems and Telecommunication, Vol. 4, No. 1, January-March 2016, pp. 19-26.#
[30] X. Ye, T.Sakurai, “Robust Similarity Measure for Spectral Clustering Based on Shared Neighbors,” ETRI Journal, vol. 38, no. 3, June. 2016, pp. 540-550.#
[31] J.Wu, F. Ding, M. Xu, Z. Mo, A..Jin. Investigating the Determinants of Decision-Making on Adoption of Public Cloud Computing in E-government. JGIM, 24(3), 2016, pp. 71-89.#
[32] S. KUMAR, "Performance Evaluation of Novel AMDF-Based Pitch Detection Scheme," ETRI Journal, vol. 38, no. 3, June. 2016, pp. 425-434. #
[33] B. Shadloo, A. Motevalian, V. Rahimi-movaghar, M.A. Esmaeili, V. Sharifi, A. Hajebi, R. Radgoodarzi, M. Hefazi, A. Rahimi- Movaghar, Psychiatric Disorders Are Associated with an Increased Risk of Injuries: Data from the Iranian Mental Health Survey, Iranian Journal of Public Health 45(5), 2016, pp. 623-635.#
