Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I David W. Pearson,Nigel C. Steele,Rudolf F. Albrech Conference proceedin

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A learning probabilistic neural network with fuzzy inference,roposed. The advantages of this network lie in the possibility of classification of the data with substantially overlapping clusters, and tuning of the activation function parameters improves the accuracy of classification. Simulation results confirm the efficiency of the proposed approach in the da

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A hybrid algorithm for weight and connectivity optimization in feedforward neural networks,or performance because of lack of expressional capacity, while a too large network fits noise or apparent relations in the data sets studied. The work required to find a parsimonious network is often considerable with respect to both time and computational effort. This paper presents a method for tr

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Mercurial

Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression,n feature extraction procedure which maps original patterns into features (factors) space of reduced, possibily very small, dimension. In this paper, we outline that Hebbian unsupervised learning of Hopfield-like neural network is a natural procedure for factor extraction. Due to this learning, fact

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