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In the paper, the deep evolving neural network and its learning algorithms (in batch and on-line mode) are proposed. The deep evolving neural network's architecture is developed based on Group Method of Data Handling approach and Least Squares Support Vector Machines with fixed number of the synaptic weights. The proposed system is simple in computational implementation, characterized by high learning...
In this paper we propose architecture of hybrid generalized additive neuro-fuzzy system. Such system is hybrid of the neuro-fuzzy system of Wang-Mendel and the generalized additive models of Hastie-Tibshirani. Proposed hybrid generalized additive neuro-fuzzy system can be used for solving different tasks of computational intelligence and data stream mining. The results of experimental modelling confirm...
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