Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model

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Zhengbing Hu 1,* Yevgeniy V. Bodyanskiy 2 Oleksii K. Tyshchenko 2 Olena O. Boiko 2

1. School of Educational Information Technology, Central China Normal University, Wuhan, China

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

* Corresponding author.


Received: 2 Jan. 2016 / Revised: 29 Apr. 2016 / Accepted: 3 Jul. 2016 / Published: 8 Oct. 2016

Index Terms

Computational Intelligence, time series prediction, neuro-neo-fuzzy System, Machine Learning, ANARX, Data Stream


An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. It's based on neo-fuzzy elements. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.

Cite This Paper

Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Olena O. Boiko, "Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.10, pp.1-10, 2016. DOI:10.5815/ijitcs.2016.10.01


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