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

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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.

DOI: https://doi.org/10.5815/ijitcs.2014.08.02

Received: 12 Aug. 2013 / Revised: 5 Feb. 2014 / Accepted: 27 Mar. 2014 / Published: 8 Jul. 2014

Index Terms

Learning Method, Cascade System, Neo-Fuzzy Neuron, Computational Intelligence


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.

Cite This Paper

Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Daria S. Kopaliani, "A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.8, pp.11-17, 2014. DOI:10.5815/ijitcs.2014.08.02


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