Identification of a Nonlinear System by Determining of Fuzzy Rules
محورهای موضوعی : Machine learning
hojatallah hamidi
1
(Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran)
Atefeh Daraei
2
(K. N.Toosi University of Technology)
کلید واژه: Data mining , Classification , Heart disease , Diagnosis , Prognosis , Treatment,
چکیده مقاله :
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.
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.#
