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

被引：26

作者：

Dalia Pano-Azucena, Ana ^{[1
]}

Tlelo-Cuautle, Esteban ^{[1
]}

Tan, Sheldon X. -D. ^{[2
]}

Ovilla-Martinez, Brisbane ^{[3
]}

Gerardo de la Fraga, Luis ^{[4
]}

机构：

[1] INAOE, Dept Elect, Puebla 72840, Mexico

[2] Univ Calif Riverside, Dept Elect & Comp Engn, Riverside, CA 92521 USA

[3] Univ Autonoma Metropolitana, Unidad Iztapalapa, Mexico City 09340, DF, Mexico

[4] CINVESTAV, Dept Comp Sci, Mexico City 07360, DF, Mexico

来源：

TECHNOLOGIES | 2018年 / 6卷 / 04期

关键词：

chaos; time series prediction; FPGA; multilayer perceptron;

D O I：

10.3390/technologies6040090

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页数：12

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