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.

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.

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.

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.

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.

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.

>=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.