Neuro granular networks with self-learning stochastic connections: fusion of neuro granular networks and learning automata theory

Authors:
Darío Maravall;Javier De Lope
Affiliations:
Perception for Computers and Robots, Universidad Politécnica de Madrid;Perception for Computers and Robots, Universidad Politécnica de Madrid
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

Citing 3
Cited 1

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Learning classifier systems

Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Varieties of learning automata: an overview

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

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

Neurocomputing

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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 presynaptic and post-synaptic learning in artificial neural networks. To illustrate the capabilities of ANLAGIS some experiments with multi-robot systems are also presented.