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首页> 外文会议>IEEE International Midwest Symposium on Circuits and Systems >Time Series Generation Using Nonlinear Autoregressive Model Artificial Neural Network Based Nonlinear Autoregressive Model Design for the Generation and Prediction of Lorenz Chaotic System
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Time Series Generation Using Nonlinear Autoregressive Model Artificial Neural Network Based Nonlinear Autoregressive Model Design for the Generation and Prediction of Lorenz Chaotic System

机译：基于非线性自回归模型人工神经网络的时间序列生成基于Lorenz混沌系统的非线性自回归模型设计

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This paper presents a Nonlinear Auto-Regressive (NAR) model design for the generation and prediction of Lorenz chaotic system using different Artificial Neural Network (ANN) architectures. Electroencephalogram (EEG) signals captured from brain activities demonstrate chaotic features. In order to theoretically understand brain functionalities, the dynamic chaotic time series outputs of a chaotic system with known system equations can be used to train ANN. And the ANN based NAR model can be used for the simulation and analysis of the chaotic features of brain activities. The ANN architecture design and optimization of the NAR chaotic system model is part of the preliminary research of a multidisciplinary brain research program. The ANN training results of different ANN architectures with 3 to 16 neurons in the hidden layer and 1 to 4 input delays of the NAR model, using training data generated with different step sizes provide important information for the selection of optimal training configuration to optimize the training performance. The research outcome is beneficial for the study of brain activities using EEG.
机译：本文提出了一种非线性自回归（NAR）模型设计，用于使用不同的人工神经网络（ANN）架构生成和预测Lorenz混沌系统。从大脑活动中捕获的脑电图（EEG）信号显示出混沌特征。为了从理论上理解大脑功能，可以使用具有已知系统方程的混沌系统的动态混沌时间序列输出来训练ANN。基于神经网络的NAR模型可用于模拟和分析大脑活动的混沌特征。 NAR混沌系统模型的ANN架构设计和优化是多学科大脑研究计划的初步研究的一部分。使用具有不同步长的训练数据，在隐藏层中具有3至16个神经元，NAR模型的输入延迟为1至4个输入延迟的不同ANN体系结构的ANN训练结果，为选择最佳训练配置以优化训练提供了重要信息表现。研究结果对于使用脑电图研究大脑活动是有益的。

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