Deep Evolving GMDH-SVM-Neural Network and its Learning for Data Mining Tasks
Galina Setlak, Yevgeniy Bodyanskiy, Olena Vynokurova, Iryna Pliss
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 141–145 (2016)
Abstract. 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 GMDH 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 speed and allows processing of data, which are fed sequentially in on-line mode. The proposed system can be used for solving a wide class of Dynamic Data Mining tasks, which are connected with non-stationary, nonlinear stochastic and chaotic signals. The computational experiments are confirmed to effectiveness of the developed approach.
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