Abstract
Fuzzy logic has been successfully used for nonlinear control systems. However, when the plant is complex or expert knowledge is not available, it is difficult to construct the rule bases of fuzzy systems. In this paper, we propose a new method of how to construct automatically the rule bases using fuzzy neural network. Whereas the conventional methods need the training data representing input-output relationship, the proposed algorithm utilizes the gradient of the performance index for the construction of fuzzy rules and the tuning of membership functions. Experimental results with the inverted pendulum show the superiority of the proposed method in comparison to the conventional fuzzy controller.
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References
Mamdani, E.H.: Apllication of Fuzzy Algorithm for Control of Simple Dynamic Plant. Inst. Electri. Eng. 121, 1569–1588 (1974)
Li, Y.F., Lau, C.C.: Developement of fuzzy algorithms for servo systems. IEEE Control Syst. Mag, 65-72 (1989)
Bernard, J.A.: Use of a rule-based system for process control. IEEE Control Syst. Mag, 3-13 (1988)
Lo Schiavo, A., Luciano, A.M.: Powerful and flexible fuzzy algorithm for nonlinear dynamic system identification. IEEE Transactions on Fuzzy Systems 9(6), 828–835 (2001)
Ozdemir, D., Akarun, L.: Fuzzy algorithms for combined quantization and dithering. IEEE Transactions on Image Processingg 10(6), 923–931 (2001)
Mousavi, P., et al.: Feature analysis and centromere segmentation of human chromosome images using an iterative fuzzy algorithm. IEEE Transactions on Biomedical Engineering 49(4), 363–371 (2002)
Roizman, O., Davydov, V.: Neuro-fuzzy algorithms for power transformers diagnostics. In: Proceedings of International Conference on Power System Technology, vol. 1, pp. 253–258 (2000)
Mumolo, E., Nolich, M., Menegatti, E.: A genetic-fuzzy algorithm for the articulatory imitation of facial movements during vocalization of a humanoid robot. In: 2005 5th IEEE-RAS International Conference on Humanoid Robots, pp. 436–441. IEEE Computer Society Press, Los Alamitos (2005)
Matsushita, S., et al.: Automatic generation control using GA considering distributed generation. In: Asia Pacific. IEEE/PES Transmission and Distribution Conference and Exhibition, vol. 3, pp. 1579–1583. IEEE, Los Alamitos (2002)
Rahmoun, A., Berrani, S.: A genetic-based neuro-fuzzy generator: NEFGEN. In: ACS/IEEE International Conference on Computer Systems and Applications, pp. 18–23. IEEE Computer Society Press, Los Alamitos (2001)
Fonseca, E.T., et al.: A neuro-fuzzy system for steel beams patch load prediction. In: 2005 Fifth International Conference on Hybrid Intelligent Systems (2005)
Fan, H., Sun, X., Zhang, M.: A novel fuzzy evaluation method to evaluate the reliability of FIN. In: The 8th International Conference on Communication Systems 2, pp. 1247–1251 (2002)
Dmitry, K., Dmitry, V.: An algorithm for rule generation in fuzzy expert systems. In: Proceedings of the 17th International Conference on Pattern Recognition 1, pp. 212–215 (2004)
Chong, A., et al.: Sparse fuzzy systems generation and fuzzy rule interpolation: a practical approach. In: The 12th IEEE International Conference on Fuzzy Systems 1, pp. 494–499. IEEE, Los Alamitos (2003)
Figueiredo, M., et al.: Comparison of Yager’s Level Set Method for Fuzzy Logic Control with Mamdani’s and Rasen’s Method. IEEE Trans. Fuzzy System 1(2) (1993)
Inoue, H., et al.: A fuzzy classifier system using hyper-cone membership functions and its application to inverted pendulum contol. In: 2002 IEEE International Conference on Systems, Man and Cybernetics 6, pp. 6–9. IEEE, Los Alamitos (2002)
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Cho, S., Kim, J., Chung, ST. (2007). Self-organizing Fuzzy Controller Based on Fuzzy Neural Network. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_19
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DOI: https://doi.org/10.1007/978-3-540-72432-2_19
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