Iryna Pliss

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The architecture of adaptive wavelet-neuro-fuzzy-network and its learning algorithm for the solving of nonstationary processes forecasting and emulation tasks are proposed. The learning algorithm is optimal on rate of convergence and allows tuning both the synaptic weights and dilations and translations parameters of wavelet activation functions. The(More)
The architecture of forecasting adaptive wavelet-neuro-fuzzy-network and its learning algorithm for the solving of nonstationary processes forecasting tasks are proposed. The learning algorithm is optimal on rate of convergence and allows to tune both the synaptic weights and dilations and translations parameters of wavelet activation functions. The(More)
Abstract: New non-conventional system of the computational intelligence is proposed. It has growing structure similar to the Cascade-Correlation Learning Architecture designed by S. E. Fahlman and C. Lebiere but differs from it in type of artificial neurons. Quadratic neurons are used as nodes in introduced architecture. These simple elements can be quickly(More)
In the paper the problem of on-line diagnostics and properties change detection of systems whose output signal is multidimensional non-stationary stochastic sequence is considered. The six-layer diagnostic neuro-fuzzy system is proposed. The first layer of this system consists of membership functions blocks, the second layer provides aggregation of(More)
In the paper, an ontogenic artificial neural network (ANNs) is proposed. The network uses orthogonal activation functions that allow significant reducing of computational complexity. Another advantage is numerical stability, because the system of activation functions is linearly independent by definition. A learning procedure for proposed ANN with(More)
Abstract: Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is(More)
Nowadays computational intelligence methods are widely spread in different tasks solving in Data Mining under uncertain, nonlinear, stochastic, chaotic and disturbed by different type of noises conditions. In the paper the hybrid neuro-neo-fuzzy system of computational intelligence is proposed. This system is distinguished by the computational simplicity,(More)
The paper introduces a Newton-type modification of temporal Hebbian rule-based learning algorithm of a self-learning spiking neural network. Similar to conventional artificial neural networks domain, the learning algorithm modification based on second-order optimization procedures allows of improving performance of the third generation neural networks. The(More)
The Gustafson-Kessel fuzzy clustering algorithm is capable of detecting hyperellipsoidal clusters of different sizes and orientations by adjusting the covariance matrix of data, thus overcoming the drawbacks of conventional fuzzy c-means algorithm. In this paper, an adaptive version of the Gustafson-Kessel algorithm is proposed. The way to adjust the(More)
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(More)