Abstract
To create a Fuzzy System from a numerical data, it is necessary to generate rules and memberships representing the analyzed set. This goal demands to break the problem into two parts: one responsible for learning the rules and another responsible for optimizing the memberships. This paper uses a Gradient-based Artificial Immune System with a different population for each of these parts. By simultaneously co-evolving these two populations, it is possible to exchange information between them enhancing the fitness of the final generated system. To demonstrate this approach, a fuzzy system for autonomous vehicle maneuvering was developed by observing a human driver.
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Pal, N.R., Chakraborty, S.: Fuzzy Rule Extraction from ID3-Type Decision Trees for Real Data. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics 31(5), 745–754 (2001)
Quinlan, J.R.: C4.5.: Programs for Machine Learning. Morgan Kaufmann, San Mateo, Inc. (1993)
Alves, R.T., Delgado, R.M., Lopes, H.S., Freitas, A.A.: An Artificial Immune System for Fuzzy-Rule Induction in Data Mining. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 1011–1020. Springer, Heidelberg (2004)
Wang, L., Mendel, J.M.: Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on Systems, Man, and Cybernetics 22(6), 1414–1427 (1992)
Abido, M.A.: A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch. Int. Journal of Electrical Power and Energy Systems 25(2), 97–105 (2003)
Li, Y., Ha, M., Wang, X.: Principle and Design of Fuzzy Controller Based on Fuzzy Learning from Examples. In: Proc. of the 1st Int. Conf. on Machine Learning and Cybernetics, vol. (3), pp. 1441–1446 (2002)
Pei, Z.: A Formalism to Extract Fuzzy If-Then Rules from Numerical Data Using Genetic Algorithms. In: Int. Symposium on Evolving Fuzzy Systems, pp. 143–147 (2006)
Abe, S., Lan, M.: Fuzzy Rules Extraction Directly from Numerical Data for Function Approximation. IEEE Transactions on Systems, Man, and Cybernetics 25(1), 119–129 (1995)
Zhao, Y., Collins, E.G., Dunlap, D.: Design of genetic fuzzy parallel parking control systems. In: Proc. American Control Conference, vol. 5, pp. 4107–4112 (2003)
de Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)
Honório, L.M., da Silva, A.M.L., Barbosa, D.A.: A Gradient-Based Artificial Immune System Applied to Optimal Power Flow Problems. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 1–12. Springer, Heidelberg (2007)
Honorio, L.M.: Virtual Manufacture Software. Information Technology Institute/UNIFEI, http://www.virtualmanufacturing.unifei.edu.br
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Vermaas, L.L.G., Honorio, L.M., Freire, M., Barbosa, D. (2009). Learning Fuzzy Systems by a Co-Evolutionary Artificial-Immune-Based Algorithm. In: Di Gesù, V., Pal, S.K., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2009. Lecture Notes in Computer Science(), vol 5571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02282-1_39
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DOI: https://doi.org/10.1007/978-3-642-02282-1_39
Publisher Name: Springer, Berlin, Heidelberg
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