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首页> 外文会议>7th Biennial Conference on Engineering Systems Design and Analysis 2004(ESDA 2004) vol.1: Advanced Energy Systems; Advanced Heat Transfer in Engineering Systems; ... >Non-linear system identification of flexible plate structures using neural networks
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Non-linear system identification of flexible plate structures using neural networks

机译：基于神经网络的柔性板结构非线性系统识别

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摘要

This paper investigates the utilisation of feedforward and recurrent neural networks for dynamic modelling of a flexible plate structure. Neuro-modelling techniques are used for non-parametric identification of the flexible plate structure based on one-step-ahead prediction. A multi layer perceptron (MLP) and Elman neural networks are designed to characterise the dynamic behaviour of the flexible plate. Results of the modelling techniques are validated through a range of tests including input/output mapping, training and test validation, mean-squared error and correlation tests. Results are presented in both time and frequency domains. Comparative performance assessments of both neuro-modelling approaches in terms of mean-squared error and estimation of the resonance modes of the system are carried out. It is noted that both techniques have been able to detect the first five vibration modes of the system successfully. Investigations also signify the advantage of a recurrent Elman network over an MLP feedforward network in modelling the flexible plate structure.
机译：本文研究了前馈和递归神经网络在柔性板结构动力学建模中的应用。神经建模技术用于基于一步一步预测的柔性板结构的非参数识别。设计了多层感知器（MLP）和Elman神经网络来表征柔性板的动态行为。建模技术的结果通过一系列测试进行验证，包括输入/​​输出映射，训练和测试验证，均方误差和相关性测试。结果显示在时域和频域。对两种神经建模方法的均方误差进行了比较性能评估，并对系统的共振模式进行了评估。注意，这两种技术都能够成功检测到系统的前五个振动模式。研究还表明，在对柔性板结构进行建模时，循环Elman网络优于MLP前馈网络。

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