Hybrid Generalized Additive Wavelet-Neuro-Fuzzy-System and Its Adaptive Learning

Abstract

In the paper, a new hybrid generalized additive wavelet-neuro-fuzzy-system of computational intelligence and its learning algorithms are proposed. This system combines the advantages of neuro-fuzzy system of Takagi-Sugeno-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.