In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is consider...In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks（RNNs） and a proposed structure called hybrid combined recurrent neural network have been used and compared.In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.展开更多
针对一类多输入多输出系统进行辨识,以"A simulation of the western basin of Lake Erie"为例,通过分析河流湖泊的水质特征,针对伊利湖湖泊水质建立数学模型,由于该环境系统为多输入多输出系统,文章采用了一种改进的BP神经...针对一类多输入多输出系统进行辨识,以"A simulation of the western basin of Lake Erie"为例,通过分析河流湖泊的水质特征,针对伊利湖湖泊水质建立数学模型,由于该环境系统为多输入多输出系统,文章采用了一种改进的BP神经网络算法,利用Matlab神经网络工具箱进行数据分析,绘出实际输出与模型输出的曲线以分析相关情况,检验建立的模型对于系统的辨识水平,给出传统BP网络和改进BP网络对该系统辨识的结果进行分析对比.文章还对不同噪声层次下的数据进行分析比较,并研究白噪声对于人工神经网络模型的影响.展开更多
为提高原子力显微镜(atomic force microscope,AFM)中微悬臂梁分布参数模型的精度,本文提出了包含非线性时空特性的改进模型,在此基础上简化控制器的结构.首先加入非线性补偿项修正传统分布参数模型;然后采用Karhunen-Loève(K–L)...为提高原子力显微镜(atomic force microscope,AFM)中微悬臂梁分布参数模型的精度,本文提出了包含非线性时空特性的改进模型,在此基础上简化控制器的结构.首先加入非线性补偿项修正传统分布参数模型;然后采用Karhunen-Loève(K–L)方法提取系统主导空间基函数,实现系统输出的时空变量分离.利用求解得到的时间系数和系统激励,建立系统时域Hammerstein模型,使系统无限维偏微分方程模型转化为时域有限维常微分方程形式,控制器的设计无需考虑空间耦合的影响;最后,利用最小二乘支持向量机结合奇异值分解法辨识模型中的参数.与传统分布参数模型进行仿真和实验结果比较,验证了方法的有效性.展开更多
文摘In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks（RNNs） and a proposed structure called hybrid combined recurrent neural network have been used and compared.In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.
文摘针对一类多输入多输出系统进行辨识,以"A simulation of the western basin of Lake Erie"为例,通过分析河流湖泊的水质特征,针对伊利湖湖泊水质建立数学模型,由于该环境系统为多输入多输出系统,文章采用了一种改进的BP神经网络算法,利用Matlab神经网络工具箱进行数据分析,绘出实际输出与模型输出的曲线以分析相关情况,检验建立的模型对于系统的辨识水平,给出传统BP网络和改进BP网络对该系统辨识的结果进行分析对比.文章还对不同噪声层次下的数据进行分析比较,并研究白噪声对于人工神经网络模型的影响.
文摘为提高原子力显微镜(atomic force microscope,AFM)中微悬臂梁分布参数模型的精度,本文提出了包含非线性时空特性的改进模型,在此基础上简化控制器的结构.首先加入非线性补偿项修正传统分布参数模型;然后采用Karhunen-Loève(K–L)方法提取系统主导空间基函数,实现系统输出的时空变量分离.利用求解得到的时间系数和系统激励,建立系统时域Hammerstein模型,使系统无限维偏微分方程模型转化为时域有限维常微分方程形式,控制器的设计无需考虑空间耦合的影响;最后,利用最小二乘支持向量机结合奇异值分解法辨识模型中的参数.与传统分布参数模型进行仿真和实验结果比较,验证了方法的有效性.
