FLEXIBLE WAVELET-NEURO-FUZZY NEURON IN DYNAMIC DATA MINING TASKS

Authors

  • Ye. - Bodyanskiy Kharkiv National University of Radio Electronics, Control Systems Research Laboratory, av. Lenina, 14, Kharkiv, 61166, Ukraine,
  • I. - Pliss Kharkiv National University of Radio Electronics, Control Systems Research Laboratory, av. Lenina, 14, Kharkiv, 61166, Ukraine,
  • O. - Vynokurova Kharkiv National University of Radio Electronics, Control Systems Research Laboratory, av. Lenina, 14, Kharkiv, 61166, Ukraine,

Keywords:

flexible neo-fuzzy neuron, flexible activation-membership function, learning algorithm, forecasting, identification

Abstract

A new flexible modification of neo-fuzzy neuron (FNFN) and adaptive learning algorithms for the tuning of its all parameters are proposed in the paper. The algorithms are interesting in that they provide on-line tuning of not
only the synaptic weights and membership functions parameters, but also forms of these functions, that provide improving approximation properties and allow to avoid the occurrence of ”gaps” in space of inputs. The proposed
algorithms have both the tracking and filtering properties, so the FNFN can be effectively used for prediction, filtering and smoothing of non-stationary stochastic and chaotic sequences. A special feature of the proposed approach
is its computational simplicity in comparison with known learning procedures for hybrid wavelet-neuro-fuzzy systems of computational intelligence.

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Published

11.11.2013

How to Cite

Bodyanskiy, Y. .-., Pliss, I. .-., & Vynokurova, O. .-. (2013). FLEXIBLE WAVELET-NEURO-FUZZY NEURON IN DYNAMIC DATA MINING TASKS. Oil and Gas Power Engineering, (2(20), 158–162. Retrieved from https://nge.nung.edu.ua/index.php/nge/article/view/269

Issue

Section

SCIENCE AND MODERN TECHNOLOGIES