ANLAGIS: Adaptive Neuron-Like Network Based on Learning Automata Theory and Granular Inference Systems with Applications to Pattern Recognition and Machine Learning

  • Darío Maravall
  • Javier de Lope
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5601)

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.

Keywords

Artificial Neural Networks Granular Computing Learning Automata Theory Pattern Recognition Machine Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Darío Maravall
    • 1
  • Javier de Lope
    • 1
  1. 1.Perception for Computers and RobotsUniversidad Politécnica de MadridSpain

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