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动态rbf神经网络
相关语句
  dynamic rbf neural network
    Single Neuron PID Control Based on Dynamic RBF Neural Network On-line Identification
    基于动态RBF神经网络在线辨识的单神经元PID控制
短句来源
    A dynamic RBF neural network algorithm used in pattern recognition
    一种用于模式识别的动态RBF神经网络算法
短句来源
    To complicated systems which are of characteristics of nonlinearity and time-variation in the industrial control fields, a self-adaptive single neuron PID control method was proposed based on the dynamic RBF neural network identification, which identified system model on-line by means of dynamic neural network identifier and acquired on-line tuning information of PID parameters, and the self-tuning of controller parameters was implemented by the single neuron controller, and the intelligence control of system was achieved.
    针对工业控制领域中复杂非线性时变系统,提出了基于动态RBF神经网络辨识的单神经元PID控制方法。 采用动态RBF神经网络辨识器在线辨识系统模型,获得PID参数在线调整信息,并由单神经元PID控制器完成控制器参数的在线自整定,实现系统的智能控制。
短句来源
  dynamic rbf neural network
    Single Neuron PID Control Based on Dynamic RBF Neural Network On-line Identification
    基于动态RBF神经网络在线辨识的单神经元PID控制
短句来源
    A dynamic RBF neural network algorithm used in pattern recognition
    一种用于模式识别的动态RBF神经网络算法
短句来源
    To complicated systems which are of characteristics of nonlinearity and time-variation in the industrial control fields, a self-adaptive single neuron PID control method was proposed based on the dynamic RBF neural network identification, which identified system model on-line by means of dynamic neural network identifier and acquired on-line tuning information of PID parameters, and the self-tuning of controller parameters was implemented by the single neuron controller, and the intelligence control of system was achieved.
    针对工业控制领域中复杂非线性时变系统,提出了基于动态RBF神经网络辨识的单神经元PID控制方法。 采用动态RBF神经网络辨识器在线辨识系统模型,获得PID参数在线调整信息,并由单神经元PID控制器完成控制器参数的在线自整定,实现系统的智能控制。
短句来源
  dynamic rbf neural network
    Single Neuron PID Control Based on Dynamic RBF Neural Network On-line Identification
    基于动态RBF神经网络在线辨识的单神经元PID控制
短句来源
    A dynamic RBF neural network algorithm used in pattern recognition
    一种用于模式识别的动态RBF神经网络算法
短句来源
    To complicated systems which are of characteristics of nonlinearity and time-variation in the industrial control fields, a self-adaptive single neuron PID control method was proposed based on the dynamic RBF neural network identification, which identified system model on-line by means of dynamic neural network identifier and acquired on-line tuning information of PID parameters, and the self-tuning of controller parameters was implemented by the single neuron controller, and the intelligence control of system was achieved.
    针对工业控制领域中复杂非线性时变系统,提出了基于动态RBF神经网络辨识的单神经元PID控制方法。 采用动态RBF神经网络辨识器在线辨识系统模型,获得PID参数在线调整信息,并由单神经元PID控制器完成控制器参数的在线自整定,实现系统的智能控制。
短句来源
  “动态rbf神经网络”译为未确定词的双语例句
    Simulation research on strip flatness and thickness control based on dynamic RBF neural networks
    基于动态RBF神经网络的板形板厚综合控制仿真研究
短句来源
    A estimation model of state-of charge (SOC) of MH-Ni battery is cited and simulated with the dynamic RBFNN. The ideal result is made, and a novel method is presented for estimation modeling of SOC of MH-Ni battery.
    基于动态RBF神经网络建立了MH-Ni电池荷电状态预估模型,通过仿真,取得了理想的结果,为MH-Ni电池荷电状态预估建模提供了新方法。
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To complicated systems which are of characteristics of nonlinearity and time-variation in the industrial control fields, a self-adaptive single neuron PID control method was proposed based on the dynamic RBF neural network identification, which identified system model on-line by means of dynamic neural network identifier and acquired on-line tuning information of PID parameters, and the self-tuning of controller parameters was implemented by the single neuron controller, and the intelligence control of system...

To complicated systems which are of characteristics of nonlinearity and time-variation in the industrial control fields, a self-adaptive single neuron PID control method was proposed based on the dynamic RBF neural network identification, which identified system model on-line by means of dynamic neural network identifier and acquired on-line tuning information of PID parameters, and the self-tuning of controller parameters was implemented by the single neuron controller, and the intelligence control of system was achieved. The simulation result indicates that the system, compared to PID control method based on the conventional RBF neural network, possesses the advantages of high precision, quick response speed and is of great adaptability and robustness.

针对工业控制领域中复杂非线性时变系统,提出了基于动态RBF神经网络辨识的单神经元PID控制方法。采用动态RBF神经网络辨识器在线辨识系统模型,获得PID参数在线调整信息,并由单神经元PID控制器完成控制器参数的在线自整定,实现系统的智能控制。仿真结果表明,与常规RBF神经网络辨识的PID控制方法相比,该方法具有控制精度高、响应速度快的优点,并且具备较强的自适应性和鲁棒性。

A method to dynamically adjust the number of hidden layer nodes is proposed based on features of the RBFNN, which includes two parts: the first part is to adjust the number of hidden layer nodes based on the mean square error and change rate of network output data, and the second part is to optimize the central value of the hidden layer and find the output layer’s weights based on the generalized inverse matrix. The newly designed RBFNN has least nodes of hidden layers and higher training speed. A mathematical...

A method to dynamically adjust the number of hidden layer nodes is proposed based on features of the RBFNN, which includes two parts: the first part is to adjust the number of hidden layer nodes based on the mean square error and change rate of network output data, and the second part is to optimize the central value of the hidden layer and find the output layer’s weights based on the generalized inverse matrix. The newly designed RBFNN has least nodes of hidden layers and higher training speed. A mathematical model for controlling strip flatness and thickness is proposed. Control simulation is executed with dynamic RBF neural network based on new model, receiving an ideal result.

基于RBF神经网络的特点提出了一种动态调节隐含层隐节点个数的方法,由2部分组成:首先以网络输出数据的均方误差及其变化率为标准来调节隐含层节点的数目,然后调节优化隐含层节点的中心值,根据广义逆矩阵的方法求出输出层权值.所设计的神经网络具有最少的隐含层节点数,提高了学习训练速度,构造了板形板厚综合控制的数学模型,采用新的模型处理方法,用动态RBF神经网络进行控制仿真,取得了理想的结果.

>=The method to control the RBFNN data centers of the hidden layer is raised in this article based on the feature of the RBFNN, it is the dynamic nearest neighbor-Clustering Algorithm. This algorithm eliminates the way factitious factor affect how to choose the data centers in extant algorithms. The RBFNN has the least nodes and high studying speed. A estimation model of state-of charge (SOC) of MH-Ni battery is cited and simulated with the dynamic RBFNN. The ideal result is made, and a novel method is presented...

>=The method to control the RBFNN data centers of the hidden layer is raised in this article based on the feature of the RBFNN, it is the dynamic nearest neighbor-Clustering Algorithm. This algorithm eliminates the way factitious factor affect how to choose the data centers in extant algorithms. The RBFNN has the least nodes and high studying speed. A estimation model of state-of charge (SOC) of MH-Ni battery is cited and simulated with the dynamic RBFNN. The ideal result is made, and a novel method is presented for estimation modeling of SOC of MH-Ni battery.

基于RBF神经网络的设计难点提出了一种动态确定隐含层节点数及数据中心的新方法,即动态最近邻聚类算法,消除了现有算法中人为因素对数据中心的影响。所设计的神经网络具有最少的隐含层节点数,结构简单,提高了网络学习训练速度。基于动态RBF神经网络建立了MH-Ni电池荷电状态预估模型,通过仿真,取得了理想的结果,为MH-Ni电池荷电状态预估建模提供了新方法。

 
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