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Neuro Granular Networks with Self-learning Stochastic Connections: Fusion of Neuro Granular Networks and Learning Automata Theory

  • Darío Maravall
  • Javier de Lope
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)

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 with multi-robot systems are also presented.

Keywords

Neuron Model Learning Automaton Target Neuron Automaton Theory Information Granule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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