MIMO非线性系统仿真的学习率

2021 IEEE 2nd International Conference on Signal, Control and Communication (SCC) Pub Date : 2021-12-20 DOI:10.1109/scc53769.2021.9768381

Farhat Yassin, Atig Asma, Z. Ali, Ben Abdennour Ridha

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引用次数: 1

摘要

本文提出了一种基于新方法的多变量非线性动力系统学习率调节仿真方案。本文的目的是调整神经仿真器(NE)的学习率，以加快收敛速度，提高精度。为了保证快速收敛和良好的估计，利用仿真误差产生的准则进行了在线自适应。实验结果表明，与使用模糊监督的现有网络相比，所设计的网络是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Learning Rate For MIMO Nonlinear System Emulation

This paper presents an emulation scheme based on a novel method to adjust the learning rate for multivariable nonlinear dynamical systems. The aim of this paper is to adapt the learning rate of the Neural Emulator (NE) in order to accelerate the convergence speed and to improve the precision degree. To ensure fast convergence and good estimation, an online adaptation is developed using a criterion generated by the error of emulation. The obtained results prouve the efficiency of the designed NE compared to those obtained with an existing one using a fuzzy supervision.

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来源期刊

2021 IEEE 2nd International Conference on Signal, Control and Communication (SCC)

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