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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
Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks
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 with high learning speed and allows processing of data, which are fed sequentially in on-line mode.
Neo-fuzzy approach for medical diagnostics tasks in online-mode
Architecture of multidimensional neo-fuzzy neuron and group of its adaptive learning algorithms was introduced for Medical Data Mining tasks in online-mode.
Hybrid neuro-neo-fuzzy system and its adaptive learning algorithm
The hybrid neuro-neo-fuzzy system can be used for solving of Data Stream Mining tasks, which connect with real time processing of nonstationary nonlinear stochastic and chaotic signals that are sequentially fed into system in on-line mode.
Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks
In this book chapter, we have considered a topical problem of intelligent energy management, which arises in the context of an intensively developed science direction—Green IT. The hybrid
Adaptive Fuzzy Clustering of Multivariate Short Time Series with Unevenly Distributed Observations Based on Matrix Neuro-Fuzzy Self-organizing Network
Proposed fuzzy clustering algorithms are enough simple in computational implementation and can be used for solving of wide class of Big Data and Data Stream Mining problems.
Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties
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.
Hybrid Generalized Additive Wavelet-Neuro-Fuzzy-System and Its Adaptive Learning
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
Deep evolving GMDH-SVM-neural network and its learning for Data Mining tasks
The deep evolving neural network's architecture is developed based on GMDH approach and least squares support vector machines with fixed number of the synaptic weights, which provide high quality of approximation in addition to the simlicity of implementation of nodes with two inputs.
Deep Hybrid System of Computational Intelligence with Architecture Adaptation for Medical Fuzzy Diagnostics
The proposed deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics allows to increase a quality of medical information processing under the condition of overlapping classes due to special adaptive architecture and training algorithms.