NEURAL NETWORK METHOD FOR PARAMETER ESTIMATION OF FRACTIONAL DISCRETE-TIME UNIFIED SYSTEMS

Zhi-Qiang Wu, Guo-Cheng Wu and Wei Zhu
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Zhi-Qiang Wu: Key Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
Guo-Cheng Wu: Key Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
Wei Zhu: Key Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China

FRACTALS (fractals), 2024, vol. 32, issue 01, 1-8

Abstract: Data-driven learning of the fractional discrete-time unified system is studied in this paper. A neural network method is suggested in the parameter estimation of fractional discrete-time chaotic systems. An optimization problem is obtained and the famous Adam algorithm is employed to train the neural network’s weights and parameters. The parameter estimation result is compared with that of the stepwise response sensitivity approach (SRSA). This paper provides a high accuracy method for parameter inverse problems. The method also can be applied to data-driven learning of other fractional chaotic systems.

Keywords: Fractional Unified System; Parameter Estimation; Neural Network; Deep Learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X2450004X

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