Prediction of Chaotic Sequence with the Adaptive Moment Estimation Algorithm Based on Maximum Correntropy Criterion

被引：0

作者：

Wang S. ^{[1
]}

Wang W. ^{[1
]}

Qian G. ^{[1
]}

机构：

[1] College of Electronic and Information Engineering//Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2019年 / 47卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Adaptive moment estimation; Chaotic time sequence; Maximum correntropy criterion; Non-Gaussian noise; Prediction accuracy; Robustness;

D O I：

10.12141/j.issn.1000-565X.180404

中图分类号：

学科分类号：

摘要：

A novel adaptive moment estimation algorithm based on maximum correntropy criterion(AdamMCC)was proposed to improve the prediction accuracy of chaotic sequence in the non-Gaussian noises. The maximum correntropy criterion was chosen as the cost function of the proposed AdamMCC owing to its robustness against non-Gaussian noises. The first and second moments of gradients in the cost function were used to adjust the weight of the parameters in the algorithm, which provides a better search direction for the optimal weight, thus improved the prediction performance of the proposed AdamMCC. Simulations on the prediction of the Mackey-Glass chaotic time sequence and Lorenz chaotic time sequence illustrate that the proposed AdamMCC can achieve better prediction performance with affordable computational complexity and maintain robustness, compared with the least mean square algorithm(LMS), the maximum correntropy criterion algorithm(MCC), and the fractional-order maximum correntropy criterion algorithm(FMCC)in the presence of non-Gaussian noises. © 2019, Editorial Department, Journal of South China University of Technology. All right reserved.

引用

页码：20 / 26and34

页数：2614

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