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  • Author: Olena Vynokurova x
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Hybrid Generalised Additive Type-2 Fuzzy-Wavelet-Neural Network in Dynamic Data Mining

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.

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Adaptive Fuzzy Clustering of Short Time Series with Unevenly Distributed Observations in Data Stream Mining Tasks

Abstract

In the paper, adaptive modifications of fuzzy clustering methods have been proposed for solving the problem of data stream mining in online mode. The clustering-segmentation task of short time series with unevenly distributed observations (at the same time in all samples) is considered. The proposed approach for adaptive fuzzy clustering of data stream is sufficiently simple in numerical implementation and is characterised by a high speed of information processing. The computational experiments have confirmed the effectiveness of the developed approach.

Open access
Neo-Fuzzy Encoder and Its Adaptive Learning for Big Data Processing

Abstract

In the paper a two-layer encoder is proposed. The nodes of encoder under consideration are neo-fuzzy neurons, which are characterised by high speed of learning process and effective approximation properties. The proposed architecture of neo-fuzzy encoder has a two-layer bottle neck” structure and its learning algorithm is based on error backpropagation. The learning algorithm is characterised by a high rate of convergence because the output signals of encoder’s nodes (neo-fuzzy neurons) are linearly dependent on the tuning parameters. The proposed learning algorithm can tune both the synaptic weights and centres of membership functions. Thus, in the paper the hybrid neo-fuzzy system-encoder is proposed that has essential advantages over conventional neurocompressors.

Open access
Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties

Abstract

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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Least Squares Support Vector Machine Based on Wavelet-Neuron/ Uz wavelet neironiem balstītā minimālo kvadrātu atbalsta vektoru mašīna/ Машина опорных векторов наименьших квадратов на основе вэйвлет-нейрона

Abstract

In this paper, a simple wavelet-neuro-system that implements learning ideas based on minimization of empirical risk and oriented on information processing in on-line mode is developed. As an elementary block of such systems, we propose using wavelet-neuron that has improved approximation properties, computational simplicity, high learning rate and capability of local feature identification in data processing. The architecture and learning algorithm for least squares wavelet support machines that are characterized by high speed of operation and possibility of learning under conditions of short training set are proposed.

Open access