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.
This work has been partially funded by the Spanish Ministry of Science and Technology, project: DPI2006-15346-C03-02.
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Maravall, D., de Lope, J. (2009). Neuro Granular Networks with Self-learning Stochastic Connections: Fusion of Neuro Granular Networks and Learning Automata Theory. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_125
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DOI: https://doi.org/10.1007/978-3-642-02490-0_125
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