Hybrid GMDH-neural network of computational intelligence. Olena Vynokurova, Iryna Pliss, Yevgeniy Bodyanskiy

Abstract. Abstract. In the paper the hybrid GMDH-neural network architectures of computational intelligence are proposed. The first architecture is GMDH-neural network based on Q-neurons with optimal learning algorithm. The second architecture is GMDH-wavelet-neural network architecture on the adaptive compartmental wavelon with computationally simple and effective learning algorithm. The learning algorithms have both following and filtering properties and allow processing of non-stationary nonlinear signals in real time. Tuning the wavelons receptive fields, including their transformations (translation, dilation, rotation, transformation membership function form) allows to improve the network approximation properties. The experiments results were compared with conventional GMDH-neural networks based on N-adalines and have shown the advantage of the suggested approach. Proposed hybrid GMDH-neural network architectures were used in non-stationary chaotic and stochastic time series forecasting, emulation and identification tasks.

Keywords. Computational intelligence, inductive modelling, GMDH-neural network, Q-neuron, wavelon, wavelet neural networks, emulation, forecasting.

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