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Yevgeniy V. Bodyanskiy

Publications and source records attributed to Yevgeniy V. Bodyanskiy.

8 recordsLinked to original sources
arXiv2017

Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing

A task of clustering data given in the ordinal scale under conditions of overlapping clusters has been considered. It's proposed to use an approach based on memberhsip and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions.

cs.LG↗
arXiv2017

Fuzzy Clustering Data Given in the Ordinal Scale

A fuzzy clustering algorithm for multidimensional data is proposed in this article. The data is described by vectors whose components are linguistic variables defined in an ordinal scale. The obtained results confirm the efficiency of the proposed approach.

cs.AI↗
arXiv2016

An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm

A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.

cs.AI↗
arXiv2016

An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes

Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.

cs.AI↗
arXiv2016

A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade

A new architecture and learning algorithms for the multidimensional hybrid cascade neural network with neuron pool optimization in each cascade are proposed in this paper. The proposed system differs from the well-known cascade systems in its capability to process multidimensional time series in an online mode, which makes it possible to process non-stationary stochastic and chaotic signals with the required accuracy. Compared to conventional analogs, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.

cs.AI↗
arXiv2016

An Evolving Neuro-Fuzzy System with Online Learning/Self-learning

An architecture of a new neuro-fuzzy system is proposed. The basic idea of this approach is to tune both synaptic weights and membership functions with the help of the supervised learning and self-learning paradigms. The approach to solving the problem has to do with evolving online neuro-fuzzy systems that can process data under uncertainty conditions. The results prove the effectiveness of the developed architecture and the learning procedure.

cs.AI↗