Abstract
In the paper, a new hybrid system of computational intelligence is proposed. This system combines the advantages of neuro-fuzzy system of Takagi-Sugeno-Kang, type-2 fuzzy logic, wavelet neural networks and generalised additive models of Hastie-Tibshirani. The proposed system has universal approximation properties and learning capability based on the experimental data sets which pertain to the neural networks and neuro-fuzzy systems; interpretability and transparency of the obtained results due to the soft computing systems and, first of all, due to type-2 fuzzy 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 generalised 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 characterised by a high speed of learning and information processing.
Highlights
Nowadays computational intelligence methods and especially hybrid systems of computational intelligence [1]– [3] are wide spread for Data Mining tasks in different areas under uncertainty, non-stationary, nonlinearity, stochastic, chaotic conditions of the investigated objects and, first of all, in control, identification, prediction, classification, emulation etc
There is a wide class of neural networks whose output signal is linearly dependent on tuning synaptic weights, and it follows that these weights can be tuned by the speed recursive procedures
The development of hybrid real-time system of computational intelligence is reasonable. Such a system will combine the advantages of traditional neural networks, neurofuzzy systems, type-2 fuzzy systems, wavelet neural networks, generalised additive models for solving a wide class of problems, which appear in the Dynamic Data Mining and Data Stream Mining
Summary
Nowadays computational intelligence methods and especially hybrid systems of computational intelligence [1]– [3] are wide spread for Data Mining tasks in different areas under uncertainty, non-stationary, nonlinearity, stochastic, chaotic conditions of the investigated objects and, first of all, in control, identification, prediction, classification, emulation etc. Neuro-fuzzy systems have undoubted advantages over neural networks and first of all the significantly smaller number of tuning synaptic weights using scatter partitioning of input space In these systems to provide the required approximating properties the synaptic weights and membership functions (centres and widths) must be tuned. The development of hybrid real-time system of computational intelligence is reasonable Such a system will combine the advantages of traditional neural networks, neurofuzzy systems, type-2 fuzzy systems, wavelet neural networks, generalised additive models for solving a wide class of problems, which appear in the Dynamic Data Mining and Data Stream Mining. This system consists of four layers of information processing; the first and second layers are similar to the layers of TSK-neuro-fuzzy system [21]–[23].
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