Abstract
A novel dynamic evolutionary clustering algorithm is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand. This algorithm searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes; at the same time, the convergence of clustering center parameters is expedited with the help of Fuzzy C-Means algorithm. Moreover, by introducing memory function and vaccine inoculation mechanism of immune system, at the same time, dynamic evolutionary clustering algorithm can converge to the optimal solution rapidly and stably. The proper fuzzy rule number and exact premise parameters are obtained simultaneously when using this efficient dynamic evolutionary clustering algorithm to identify fuzzy models. The effectiveness of the proposed fuzzy modeling method based on dynamic evolutionary clustering algorithm is demonstrated by simulation examples, and the accurate non-linear fuzzy models can be obtained when the method is applied to the thermal processes.
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References
Bertotti, G.: Identification of the Damping Coefficient in Landau-Lifshitz Equation. Physical B, 102–105 (2001)
Mau, S T.: A Subspace Modal Superposition Method for Non-classical Damping Systems. Earthquake Eng Struct Dyn, 931–942 (1998)
Zhang, T.J., Lu, J.H., Yu, K.J.: A New Approach for Predictive Control Based on Fuzzy Decision-making and Its Application to Thermal Process. In: Proceedings of the CSEE, pp. 179–184 (2004)
Liu, Z.Y., Lu, J.H., Chen, L.J.: A Novel RBF Neural Network and Its Application in Thermal Processes Modeling. In: Proceedings of the CSEE pp. 8–122 (2002)
Feng, W.X., Chen, X.: Amethod for Estimating of Damping Matrix of Structural Dynamic Systems. J of Guangdong University of Technology, 6–11 (2001)
Maulik, U., Bandyopdhyay, S.: Performance Evaluation of Some Clustering Algorithms and Validity Indices. IEEE Trans on Pattern Analysis and Machine Intelligence 1650–1654 (2002)
Hou, Y.W., Shen, J., Li, Y.G.: A Simulation Study on Load Modeling of A Thermal Power Unit Based on Wavelet Neural Networks. In: Proceedings of the CSEE, 220–224 (2003)
Jiang, W.J.: Research on the Learning Algorithm of BP Neural Networks Embedded in Evolution Strategies. In: WCICA’2005, pp. 222–227 (2005)
Xu, Q., Wen, X.R.: High Precision Direct Integration Scheme for Structural Dynamic Load Identification. Chinese J of Computational Mechanics, 53–57 (2002)
Wu, J.Y., Wang, X.C.: A Parallel Genetic Design Method With Coarse Grain. Chinese J of Computational Mechanics, 148–153 (2002)
Li, S.J., Liu, Y.X.: Identification of Structural Vibration Parameter Based on Genetic Algorithm. J of Chinese University of Mining Science and Technology, 256–260 (2001)
Gomez-Skarmeta, A.F., Delgado, M., Vila, M.A.: About the Use of Fuzzy Clustering Techniques for Fuzzy Model Identification?Fuzzy Sets and Systems, 179–188 (1999)
Furukawa, T.: An Automated System for Simulation and Parameter Identification of Inelastic Constitute Models. Computer Methods Appl. Mech. Eng. 2235–2260 (2002)
Deng, H., Sun, Z.Q., Sun, F.C.: The Fuzzy Cluster Identification Algorithm.Control Theory and Applications, 171–175 (2001)
Zhao, L., Tsujimura, Y., Gen, M.: Genetic Algorithm for Fuzzy Clustering. In: Proceedings of IEEE International Conference on Evolutionary Computation, IEEE Computer Society Press, Los Alamitos (1996)
Liu, J., Zhong, W.C., Liu, F., et al.: A Novel Clustering Based on the Immune Evolutionary Algorithm. ACTA Electronic SWICA, 1860–1072 (2001)
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Xu, Y., Jiang, W. (2007). Research a New Dynamic Clustering Algorithm Based on Genetic Immunity Mechanism. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_69
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DOI: https://doi.org/10.1007/978-3-540-74205-0_69
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