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首页> 外文期刊>Mechanical systems and signal processing >Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks
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Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks

机译：基于相关函数和径向基函数神经网络的级联进化算法用于非线性系统辨识

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摘要

The present work introduces a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models with an improved objective function based on the residuals and its correlation function coefficients. We show the results when the proposed methodology is applied to model a magnetorheological damper, with real acquired data, and other two well-known benchmarks. The canonical genetic and differential evolution algorithms are used in cascade to decompose the problem of defining the lags taken as the inputs of the model and its related parameters based on the simultaneous minimization of the residuals and higher orders correlation functions. The inner layer of the cascaded approach is composed of a population which represents the lags on the inputs and outputs of the system and an outer layer represents the corresponding parameters of the RBFNN. The approach is able to define both the inputs of the model and its parameters. This is interesting as it frees the designer of manual procedures, which are time consuming and prone to error, usually done to define the model inputs. We compare the proposed methodology with other works found in the literature, showing overall better results for the cascaded approach.
机译：本工作介绍了一种基于径向基函数神经网络（RBFNN）模型的系统识别输入选择和参数估计程序，该模型具有基于残差及其相关函数系数的改进目标函数。当将所提出的方法应用于具有实际采集数据和其他两个众所周知的基准的磁流变阻尼器建模时，我们将显示结果。规范遗传和差分进化算法用于级联分解基于残差和高阶相关函数的同时最小化来定义滞后作为模型及其相关参数输入的问题。级联方法的内层由代表系统输入和输出滞后的总体组成，外层代表RBFNN的相应参数。该方法能够定义模型的输入及其参数。这很有趣，因为它使设计人员摆脱了通常耗时且容易出错的手动过程，这些过程通常是在定义模型输入时完成的。我们将提出的方法与文献中发现的其他工作进行了比较，显示了级联方法的总体较好结果。

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