Optimization of fuzzy systems using group-based evolutionary algorithm
Pages 291 - 298
Abstract
This paper proposes a group-based evolutionary algorithm (GEA) for the fuzzy system (FS) optimization. Initially, we adopt an entropy measure method to determine the number of rules. Fuzzy rules are automatically generated from training data by entropy measure. Subsequently, the GEA is performed to optimize all the free parameters for the FS design. In the evolution process, a FS is coded as an individual. All individuals based on their performance are partitioned into a superior group and an inferior group. The superior group, which is composed of individuals with better performance, uses a global evolution operation to search potential individuals. In the inferior group, individuals with a worse performance employ the local evolution operation to search better individuals near the current best individual. Finally, the proposed FS with GEA model (FS-GEA) is applied to time series forecasting problem. Results show that the proposed FS-GEA model obtains better performance than other algorithm.
References
[1]
Lin, C. T., Lee, C. S. G.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent System. Prentice-Hall, Englewood Cliffs (1996)
[2]
Han, M. F., Lin, C. T., Chang, J.Y.: A Compensatory Neurofuzzy System with Online Constructing and Parameter Learning. In: Proc. of 2010 IEEE International Conference on Syst., Man, and Cybern., pp. 552-556 (2010)
[3]
Chen, C. H., Lin, C. J., Lin, C. T.: A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control. IEEE Trans. Fuzzy Syst. 16, 1362-1378 (2008)
[4]
Juang, C. F., Chang, P. H.: Designing Fuzzy Rule-Based Systems Using Continuous Ant Colony Optimization. IEEE Trans. Fuzzy Syst. 18, 138-149 (2010)
[5]
Sanchez, E., Shibata, T., Zadeh, L. A.: Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives. World Scientific, Singapore (1997)
[6]
Chou, C. H.: Genetic Algorithm-Based Optimal Fuzzy Controller Design in The Linguistic Space. IEEE Trans. Fuzzy Syst. 14, 372-385 (2006)
[7]
Juang, C. F., Hsiao, C. M., Hsu, C. H.: Hierarchical Cluster-Based Multispecies Particle-Swarm Optimization for Fuzzy-System Optimization. IEEE Trans. Fuzzy Syst. 18, 14-26 (2010)
[8]
Juang, C. F.: A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design. IEEE Trans. Syst., Man, Cybern. B 34, 997-1006 (2004)
[9]
Li, C., Chiang, T. W.: Complex Fuzzy Model with PSO-RLSE Hybrid Learning Approach to Function Approximation. International Journal of Intelligent Information and Database Systems 5, 409-430 (2011)
[10]
Lin, C. J., Lee, C.Y.: Non-Linear System Control Using A Recurrent Fuzzy Neural Network Based on Improved Particle Swarm Optimization. International Journal of Systems Science 41, 381-395 (2010)
[11]
Das, S., Suganthan, P. N.: Differential Evolution: A Survey of the State-of-the-Art. IEEE Trans. Evol. Comput. 14, 4-31 (2011)
[12]
Lin, C. T., Han, M. F., Lin, Y.Y., Liao, S. H., Chang, J.Y.: Neuro-Fuzzy System Design Using Differential Evolution with Local Information. In: 2011 IEEE International Conference on Fuzzy Systems, pp. 1003-1006 (2011)
[13]
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Trans. Evol. Comput. 10, 646-659 (2006)
[14]
Han, M. F., Lin, C. T., Chang, J.Y.: Group-Based Differential Evolution for Numerical Optimization Problems. International Journal of Innovative Computing, Information and Control 9, 2 (2013)
[15]
Chen, C. H., Lin, C. J., Lin, C. T.: Nonlinear System Control Using Adaptive Neural Fuzzy Networks Based on a Modified Differential Evolution. IEEE Trans. Syst. Man Cybern. Part C, Appl. Rev., 459-473 (2009)
[16]
Lin, C.-J., Chen, C.-H., Lin, C.-T.: Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems. IEEE Trans. Fuzzy Syst. 16(6), 1476-1490 (2008)
[17]
Lin, C.-J., Wu, C.-F., Lee, C.-Y.: Design of a Recurrent Functional Neural Fuzzy Network Using Modified Differential Evolution. International Journal of Innovative Computing, Information and Control 7(1), 669-683 (2011)
[18]
Cho, K. B., Wang, B. H.: Radial Basis Function Based Adaptive Fuzzy Systems and Their Applications to System Identification. Fuzzy Sets Syst. 83, 325-339 (1996)
[19]
Kim, J., Kasabov, N. K.: HyFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamic systems. Neural Netw. 12, 1301-1319 (1999)
[20]
Nauk, D., Kruse, R.: Neuro-Fuzzy Systems for Function Approximation. Fuzzy Sets Syst. 101, 261-271 (1999)
[21]
Wu, S., Er, M. J.: Dynamic Fuzzy Neural Networks--A Novel Approach to Function Approximation. IEEE Trans. Syst., Man, Cybern. B 30, 358-364 (2000)
[22]
Karr, C. L.: Design of An Adaptive Fuzzy Logic Controller Using A Genetic Algorithm. In: Proc. 4th Conf. Genetic Algorithms, pp. 450-457 (1991)
[23]
Juang, C. F., Lin, J.Y., Lin, C. T.: Genetic Reinforcement Learning Through Symbiotic Evolution for Fuzzy Controller Design. IEEE Trans. Syst., Man, Cybern., Part B 30, 290-302 (2000)
Recommendations
Dynamic group-based differential evolution using a self-adaptive strategy for global optimization problems
This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimization problems. The GDE algorithm provides a generalized evolution process based on two mutation operations to enhance search capability. Initially, all ...
Centroid Opposition-Based Differential Evolution
The capabilities of evolutionary algorithms (EAs) in solving nonlinear and non-convex optimization problems are significant. Differential evolution (DE) is an effective population-based EA, which has emerged as very competitive. Since its inception in ...
A novel differential evolution using a mixed mutation strategy
ICIMCS '10: Proceedings of the Second International Conference on Internet Multimedia Computing and ServiceDifferential Evolution (DE) is well-known as a simple and efficient evolutionary algorithm for global optimization problems. However, the mutation strategies used in DE greatly affect its performance. Although many mutation operators have been proposed ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2012
709 pages
ISBN:9783642344862
Sponsors
- ExxonMobil
- QAPCO: QAPCO
- United Development: United Development Co.
- Qatar Petroleum: Qatar Petroleum
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 12 November 2012
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Feb 2025