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rbf神经网络模型
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  rbf neural network model
     The Research on RBF Neural Network Model of Diesel Fuel Blending
     柴油调合RBF神经网络模型研究
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     Strategies to the noise contained in experimental data in RBF neural network model
     实验数据RBF神经网络模型中噪声的处理方法
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     RBF Neural Network Model for Combinatorial Nonperiodic Defected Ground Structures
     组合式非周期缺陷接地结构的RBF神经网络模型
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     Optimizing the RBF neural network model for an industrial objective by the genetic algorithms
     遗传算法优化工业对象的RBF神经网络模型
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     (2) The RBF neural network model has better convergence ability and impending speed than the BP neural network model.
     (2)RBF神经网络模型的收敛能力和逼近速度优于BP神经网络模型.
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  rbf never network model
     The paper introduces RBF never network model. The never network model of concrete strength loss is established with RBF never network after cycle of freezing and thawing. This RBF model takes the water-cement ratio, specific cement consumption, sand ratio and concrete quality loss after freezing and thawing as input, takes the strength loss after freezing and thawing as export.
     本文介绍了R BF神经网络模型,并运用R BF神经网络的理论和方法,建立了配合比对冻融循环后混凝土强度损失的神经网络模型,该R BF模型以水灰比、单位水泥用量、砂率及冻融后混凝土质量损失为输入,以冻融循环后混凝土的强度损失为输出;
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  “rbf神经网络模型”译为未确定词的双语例句
     Modelling of Springback with RBF Neural Network Based on Experiments in Flanging Operation
     基于薄板翻边回弹试验的RBF神经网络模型
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     By means of learning and training on RBF neural networks model, the average error of training set is 0.32% and that of testing set is 6.48%. RBF neural networks model is better comparatively.
     对于 RBF神经网络模型 ,经过网络学习和训练 ,训练集平均误差仅为 0 .32 % ,测试集误差为 6 .48% ,效果比较理想
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     Based on the working conditions of mixer,prediction model of RBF network with 4 input vectors and 1 output vectors is set up,and the model is proved by experimental results.
     根据搅拌机的实际工作状况,建立了4个输入节点、1个输出节点的RBF神经网络模型,通过19组试验,验证了模型的可靠性。
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     The results show that the experimental results are closed to those of prediction and the RBF neural network is the more accurate and rapid method to predict the compression strength of concrete.
     结果表明,实测结果与预测结果相接近,RBF神经网络模型是一种较准确的快速预测混凝土抗压强度的方法。
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     After comparing, RBF model is adopted to forecast the price of the stock, and the stocks are gained which fit to portfolio.
     本文通过比较分析后,采用RBF神经网络模型对证券价格进行了预测,并在这个基础上得到了适用于组合的证券。
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  rbf neural network model
For the RBF neural network model, which is more effective for monitoring weld quality than the others, the average error validated is 2.88% and the maximal error validated is under 10%.
      
A radial basis function (RBF) neural network model was trained by the input-output data of impedance.
      
The RBF neural network model was used to test the fuzzy neural network controller.
      
A radial basis function (RBF) neural network model based predictive control scheme is developed for multivariable nonlinear systems in this paper.
      


A new type of non linear self repairing control strategy based on model following method using radial basis function (RBF) neural networks is presented. This method can make the outputs of impaired system tracking those of reference model accurately without knowing the location and damage degree of failure, and a RBF neural network controller is used to compensate non linear dynamics caused by failure. The structure of the controller is simple and the neural compensator does not require a complex iterative...

A new type of non linear self repairing control strategy based on model following method using radial basis function (RBF) neural networks is presented. This method can make the outputs of impaired system tracking those of reference model accurately without knowing the location and damage degree of failure, and a RBF neural network controller is used to compensate non linear dynamics caused by failure. The structure of the controller is simple and the neural compensator does not require a complex iterative procedure, so it can be carried out on line. Since the conditions of perfect model following (PMF) are satisfied, the proposed method can be applied to self repaining control for a large class of nonlinear system. At last, this method is demonstrated in an aircaft longitudinal contorl system. Simulation results reveal that this method has good reconfigurable performance and robustness.

提出一种基于径向基函数(RBF)神经网络的模型跟随非线性自修复控制方法。该方法可不必精确已知故障的位置及程度,即可重构控制律使系统在故障情况下的输出精确跟踪期望参考模型的输出,并采用神经网络控制器以补偿故障引起的非线性因素的影响。仿真验证表明,本文方法可保证闭环系统具有良好的重构性和鲁棒性。

Aim The RFB (radial hats function) netal network was studied for the model indentificaiton of an ozonation/BAC system. Methods The optimal ozone's dosage and the remain time in carbon tower were analyzed to build the neural network model by which the expected outflow CODM can be acquired under the inflow CODM condition. Results The improved self-organized learning algorithm can assign the centers into appropriate places , and the RBF network's outputs at the sample points fit the experimental data very well....

Aim The RFB (radial hats function) netal network was studied for the model indentificaiton of an ozonation/BAC system. Methods The optimal ozone's dosage and the remain time in carbon tower were analyzed to build the neural network model by which the expected outflow CODM can be acquired under the inflow CODM condition. Results The improved self-organized learning algorithm can assign the centers into appropriate places , and the RBF network's outputs at the sample points fit the experimental data very well. Conclusion The model of ozonation /BAC system based on the RBF network am describe the relationshipamong various factors correctly, a new prouding approach tO the wate purification process is provided.

目的使用RBF(径向基函数)神经网络辨识臭氧氧化、生物活性炭水处理过程的机理模型.方法在一定原水CODM条件下,分析臭氧投量和生物活性炭塔停留时间对出水CODM的影响作用,建立神经网络模型.结果仿真结果表明改进自组织算法能够合理配置RBF网络中心点集,神经网络模型输出能够有效逼近实验数据样本.结论RBF神经网络模型能够准确描述臭氧氧化、生物活性炭水处理过程中的参数关系,为水处理过程的深入研究提供了新途径.

A new type of Takagi Sugeno (T S) fuzzy radial basis function (RBF) in neural network is presented. A fuzzy optimum cluster algorithm in the input space is discussed. The simulation results show that the T S fuzzy RBF neural network approximates nonlinear function with any multi variable with any degree of accuracy, verifying the cluster algorithm being effective and available.

提出T-S型模糊RBF神经网络模型结构,讨论该模型参数的输入空间模糊最优聚类学习算法.仿真结果验证了学习算法的有效性和可行性,表明T-S型模糊RBF神经网络可逼近任意多变量非线性函数.

 
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