Abstract
In this paper the fusion of artificial neural networks, granular computing and learning automata theory is proposed and we present as a final result ANLAGIS, an adaptive neuron-like network based on learning automata and granular inference systems. ANLAGIS can be applied to both pattern recognition and learning control problems. Another interesting contribution of this paper is the distinction between pre-synaptic and post-synaptic learning in artificial neural networks. To illustrate the capabilities of ANLAGIS some experiments on knowledge discovery in data mining and machine learning are presented. Previous work of Jang et al. [1] on adaptive network-based fuzzy inference systems, or simply ANFIS, can be considered a precursor of ANLAGIS. The main, novel contribution of ANLAGIS is the incorporation of Learning Automata Theory within its structure.
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References
Jang, J.S.R.: ANFIS Adaptive network-based fuzzy inference systems. IEEE Trans. on Systems, Man and Cybernetics 23(3) (1992)
Kasabov, N.K.: Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, ch. 2. MIT Press, Cambridge (1996)
Holland, J.H.: Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. In: Michaiski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning II, pp. 593–623. Morgan Kaufmann, San Francisco (1996)
Butz, M.V.: Learning Classifier Systems. In: Proc. GECCO 2008, pp. 2367–2388 (2008)
Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): IWLCS 2002. LNCS, vol. 2661. Springer, Heidelberg (2003)
Thathachar, M., Sastry, P.: Varieties of Learning automata: An Overview. IEEE Trans. on Systems, Man and Cybernetics, Part B 32(6), 711–722 (2002)
Neurath, O.: Protocol sentences, Logical Positivism (The Library of Philosophical Movements), pp. 199–208. A.J. Ayer Free Press (1959)
Michie, D., Chambers, R.A.: Boxes: An experiment in adaptive control. In: Dale, E., Michie, D. (eds.) Machine Intelligence 2, pp. 137–152. Oliver & Boyd (1968)
Narendra, K., Thathachar, M.: Learning Automata - A Survey. IEEE Trans. on Systems, Man, and Cybernetics 4(4), 323–334 (1974)
Zadeh, L.: From Computing with Numbers to Computing with Words — From manipulation of measurements to manipulation of perceptions. IEEE Trans. on Circuits and Systems–II Fundamental Theory and Applications 4, 105–119 (1999)
Bargiela, A., Pedrycz, W.: Toward a theory of granular computing for human-Centered information processing. IEEE Trans. on Fuzzy Systems 16(2), 320–330 (2008)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annual Eugenics 7, Part II, 179–188 (1936); also in Contributions to Mathematical Statistics (John Wiley, NY 1950)
Mangasarian, O.L., Wolberg, W.H.: Cancer diagnosis via linear programming. SIAM News 23(5), 1, 18 (1990)
Weiss, S.M., Kapoulas, I.: An empirical comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods. In: Proc. Eleventh International Joint Conference on Artificial Intelligence, pp. 781–787
Maravall, D., De Lope, J.: Neuro granular networks with self-learning stochastic connections: Fusion of neuro granular networks and learning automata theory. In: Kasabov, N.K., et al. (eds.) Proc. ICONIP 2008 (2008) (in press)
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Maravall, D., de Lope, J. (2009). ANLAGIS: Adaptive Neuron-Like Network Based on Learning Automata Theory and Granular Inference Systems with Applications to Pattern Recognition and Machine Learning. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy. IWINAC 2009. Lecture Notes in Computer Science, vol 5601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02264-7_11
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DOI: https://doi.org/10.1007/978-3-642-02264-7_11
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