Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Toward a Theory of Granular Computing for Human-Centered Information Processing
IEEE Transactions on Fuzzy Systems
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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.