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FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Department of Electronics, INAOE, Puebla 72840, Mexico
Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA
Unidad Iztapalapa, Universidad Autónoma Metropolitana, Mexico City 09340, Mexico
Department of Computer Sciences, CINVESTAV, Mexico City 07360, Mexico
Author to whom correspondence should be addressed.
Technologies 2018, 6(4), 90;
Received: 16 August 2018 / Revised: 18 September 2018 / Accepted: 28 September 2018 / Published: 1 October 2018
(This article belongs to the Special Issue Modern Circuits and Systems Technologies on Electronics)
PDF [1248 KB, uploaded 1 October 2018]


Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series. View Full-Text
Keywords: chaos; time series prediction; FPGA; multilayer perceptron chaos; time series prediction; FPGA; multilayer perceptron

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Pano-Azucena, A.D.; Tlelo-Cuautle, E.; Tan, S.X.-D.; Ovilla-Martinez, B.; De la Fraga, L.G. FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction. Technologies 2018, 6, 90.

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