Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning

Authors:
Darío Maravall;Javier de Lope
Affiliations:
Department of Artificial Intelligence, Faculty of Computer Science, Universidad Politécnica de Madrid, Madrid, Spain and Centro de Automática y Robótica (UPM - CSIC), Universidad Po ...;Centro de Automática y Robótica (UPM - CSIC), Universidad Politécnica de Madrid, Madrid, Spain
Venue:
Neurocomputing
Year:
2011

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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. The main, novel contribution of ANLAGIS is the incorporation of Learning Automata Theory within its structure; the paper includes also a novel learning scheme for stochastic learning automata.