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rbf辨识
相关语句
  rbf identification
     Design and Realization of Fuzzy Neural Network Controller Based on RBF Identification
     基于RBF辨识的模糊神经网络控制器的设计与实现
短句来源
     The control arithmetic of RBF identification network was discussed and a new PID controller was raised. This controller used the PID on-line parameters adjustment of nerve cell self adaptation,and used the BFR network to identify on-line the controlled object.
     讨论了RBF辨识网络的控制算法,并提出了一种新型PID控制器,该控制器利用神经元自适应PID的在线参数调整,采用RBF网络对被控对象在线辨识。
短句来源
     At the same time, the typical nonlinear system simulation experiments were made, and also this method is a contrast to RBF neural networks based-on genetic algorithm (GA-RBF) and RBF identification effects. The simulation results show this MPSO-RBF method has quicker optimization speed and better approximation performance, and it obtains higher identification precision.
     经典型非线性系统仿真试验,并与GA-RBF和RBF辨识效果进行了对比,结果表明基于MPSO-RBF的混合优化方法较GA-RBF和RBF优化速度快、逼近性能好,可以达到更优的辨识精度。
短句来源
  “rbf辨识”译为未确定词的双语例句
     Research on CMAC Controller for Servo System Based on RBF Identifier
     基于RBF辨识的伺服系统CMAC控制
短句来源
     Thirdly, there introduced an improved fuzzy neural network control basedon identification of RBF. The scaling factor for fuzzification and the scalinggain were imbedded in the network, which composed the network's input layerand output layer.
     再次,给出了一种基于RBF辨识的改进型模糊神经网络控制,即将量化因子和比例因子嵌入到模糊神经网络中,构成网络的输入层和输出层;
短句来源
     Instead of generally approximate method, the networkemployed the radial basis function (RBF) neural network to offer preciseinformation of Jacobian for the system.
     并采用RBF辨识网络取代传统的近似做法,为系统提供精确的Jacobian信息;
短句来源
     An improved neural network PID control based on RBF neural network is brought forward. Quadratic model in optimization is used as the guideline in the regulation of the coefficient. By combining them a PID controller is formulated.
     提出一种基于RBF辨识神经网络算法的改进神经网络PID控制,应用最优控制理论中的二次型性能指标加入到控制算法中的加权系数学习修正部分,将RBF与单神经元相结合构成PID控制器,通过Matlab对指定对象仿真控制,得到了良好的效果。
短句来源
     Simulation results show that the identification error is within 1.5%,which is sufficient for coal combustion systems. Also,the method is faster than a simple radial basis fuction network so it can be applied to online coal LHV identification for combustion systems.
     仿真结果表明,该辨识方法的辨识误差在1.5%以内,具有良好的辨识精度,在速度上也优于单独的RBF辨识算法,可以应用于热力系统煤种发热量在线辨识。
短句来源
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  相似匹配句对
     An Identification Model of Aeroengine Based on the RBF Network
     基于RBF网络的航空发动机辨识模型
短句来源
     RBF-Based Identification of combustion controlling system
     基于RBF网络的燃烧系统辨识
     On the Identification of Linear Discrete Event Dynamic Systems
     线性离散事件动态系统的辨识
短句来源
     Mathematical Model Identification of Hydraulic System
     液压系统数学模型的辨识
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An improved neural network PID control based on RBF neural network is brought forward. Quadratic model in optimization is used as the guideline in the regulation of the coefficient. By combining them a PID controller is formulated. Simulating by Matlab on an object, a good result is gained. Compared with previous neural network PID control, the improved arithmetic has better real time capability and learning rate.

提出一种基于RBF辨识神经网络算法的改进神经网络PID控制,应用最优控制理论中的二次型性能指标加入到控制算法中的加权系数学习修正部分,将RBF与单神经元相结合构成PID控制器,通过Matlab对指定对象仿真控制,得到了良好的效果。比起以往的神经网络PID控制,改进的算法在学习速度和实时性都得到了提高。

Coal) characteristics greatly affect energy system economics and cleanliness.A multiple model method identification was developed to precisely identify the lower heating values(LHV) of various coal types.The initial identification process used an improved K-means clustering method to reduce the model search range for the precise identification.The precise identification used a radial basis fuction neural network algorithm with self-adjusting latent nodes.Simulation results show that the identification error...

Coal) characteristics greatly affect energy system economics and cleanliness.A multiple model method identification was developed to precisely identify the lower heating values(LHV) of various coal types.The initial identification process used an improved K-means clustering method to reduce the model search range for the precise identification.The precise identification used a radial basis fuction neural network algorithm with self-adjusting latent nodes.Simulation results show that the identification error is within 1.5%,which is sufficient for coal combustion systems.Also,the method is faster than a simple radial basis fuction network so it can be applied to online coal LHV identification for combustion systems.

为了保证热力系统稳定运行,提高锅炉安全寿命,控制污染物,该文利用多模型思想,对煤种低位发热值进行初步辨识和精确辨识。初步辨识中,采用改进的K均值聚类算法,快速辨识出煤种类型;精确辨识中,利用初步辨识的结果优化发热量辨识模型,减少模型搜索范围,采用自动调节隐节点和参数的径向基函数(RBF)神经网络算法。仿真结果表明,该辨识方法的辨识误差在1.5%以内,具有良好的辨识精度,在速度上也优于单独的RBF辨识算法,可以应用于热力系统煤种发热量在线辨识。

A kind of CMAC controller for servo system based on RBF identifier was proposed. The controller consists of the RBF identifier, the single neuron PID controller and the CMAC feedforward controller. The RBF neural network is used to identify the model of the plant and adjust the single neuron PID controller’s parameter. The suitable parameter of the controller is given as fast as possible by used searching. This method could shorten transient response time clearly. The simulation experiment proves the method...

A kind of CMAC controller for servo system based on RBF identifier was proposed. The controller consists of the RBF identifier, the single neuron PID controller and the CMAC feedforward controller. The RBF neural network is used to identify the model of the plant and adjust the single neuron PID controller’s parameter. The suitable parameter of the controller is given as fast as possible by used searching. This method could shorten transient response time clearly. The simulation experiment proves the method can improve control precision and the response rapidness.

提出一种基于RBF辨识的伺服系统CMAC复合控制器,并进行了仿真研究。采用RBF神经网络辨识被控对象模型,根据辨识结果调节单神经元控制器的参数,由单神经元PID控制器与小脑模型前馈控制器组成复合控制结构,通过搜索使控制器尽快地进入合适的参数空间,实现了控制的快速性要求。仿真结果表明,该控制方法能够缩短系统暂态响应时间,提高系统的动态跟踪精度,增加系统鲁棒稳定性。

 
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