-In the paper, a new flexible modification of neofuzzy neuron, neuro-fuzzy network based on these neurons and adaptive learning algorithms for the tuning of their all parameters are proposed. The algorithms are of interest because they ensure the on-line tuning of not only the synaptic weights and membership function parameters but also forms of these functions that provide improving approximation properties and allow avoiding the occurrence of "gaps" in the space of inputs.

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An optimal online learning algorithm of a wavelet neural network is proposed. The algorithm provides not only the tuning of synaptic weights in real time, but also the tuning of dilation and translation factors of daughter wavelets. The algorithm has both tracking and smoothing properties, so the wavelet networks trained with this algorithm can be efficiently used for prediction, filtering, compression and classification of various non-stationary noisy signals.

In the paper, a new hybrid generalized additive wavelet-neurofuzzy-system of computational intelligence and its learning algorithms are proposed. This system combines the advantages of neuro-fuzzy system of TakagiSugeno-Kang, wavelet neural networks and generalized additive models of Hastie-Tibshirani. The proposed system has universal approximation properties and learning capabilities which pertain to the neural networks and neuro-fuzzy systems; interpretability and transparency of the obtained results due to the soft computing systems; possibility of effective description of local signal and process features due to the application of systems based on wavelet transform; simplicity and speed of learning process due to generalized additive models. 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. Such a system is sufficiently simple in numerical implementation and is characterized by a high speed of learning and information processing.