An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm

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Yevgeniy V. Bodyanskiy 1,* Oleksii K. Tyshchenko 1 Daria S. Kopaliani 1

1. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

* Corresponding author.


Received: 22 Jun. 2014 / Revised: 1 Oct. 2014 / Accepted: 17 Nov. 2014 / Published: 8 Jan. 2015

Index Terms

Learning Method, Neuro-Fuzzy System, Extended Neo-Fuzzy Neuron, Computational Intelligence


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 and solves prediction, filtering and smoothing tasks of non-stationary “noisy” stochastic and chaotic signals. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.

Cite This Paper

Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Daria S. Kopaliani, "An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.2, pp.21-26, 2015. DOI:10.5815/ijisa.2015.02.03


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