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  rbf neural network
At the same time, linear regression, nonlinear regression and radial basis function (RBF) neural network models are set up to evaluate weld quality between the selected parameters and tensile-shear strength.
      
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%.
      
To facilitate a valid control strategy design, this paper tries to avoid the internal complexities and presents a modelling study of SOFC performance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA).
      
During the process of modelling, the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters.
      
Furthermore, it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.
      
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  rbf neural networks
During the process of modelling, the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters.
      
The validity and accuracy of modelling are tested by simulations, whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA.
      
Optimization and characterization of electromagnetically coupled patch antennas using RBF neural networks
      
A practical method of estimation for the internal-resistance of polymer electrolyte membrane fuel cell (PEMFC) stack was adopted based on radial basis function (RBF) neural networks.
      
This paper is concerned with the types of invariance exhibited by Radial Basis Function (RBF) neural networks when used for human face classification, and the generalisation abilities arising from this behaviour.
      
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  radial basis function neural network
Shape identification of electrocardiographic ST segment based on radial basis function neural network
      
In this paper, we introduce a computerized automatic identification method of the electrocardiographic ST segment shape with radial basis function neural network based on adaptive fuzzy system, which has a better effect than other methods.
      
Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network
      
The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper.
      
Fuzzy self-adaptive radial basis function neural network-based control of a seven-link redundant industrial manipulator
      
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  rbf nn
A new algorithm using a RBF NN (radial basis function neural network) is proposed to predict this traffic chaos.
      
  其他


This paper presents a new approach on identifying cylinder pressure of internal combustion engine from the engine cylinder head vibration signals based on radial basis function (RBF) neural network.The identification principle of this approach and its process are presented at first.And according to the operation characteristic of internal combustion engine,the parameters of RBF neural network are effevtively set and the nonlinear mapping relationship between the vibration signals and cylinder pressure signals...

This paper presents a new approach on identifying cylinder pressure of internal combustion engine from the engine cylinder head vibration signals based on radial basis function (RBF) neural network.The identification principle of this approach and its process are presented at first.And according to the operation characteristic of internal combustion engine,the parameters of RBF neural network are effevtively set and the nonlinear mapping relationship between the vibration signals and cylinder pressure signals is established.Then,the validation of this approach on cylinder pressure identification from vibration signals is demonstrated on experimental data.The results show that the exactness of the waveform and the characteristic values of identified cylinder pressure are high and robust.At last,some relative problems are discussed.

提出了一种新的利用内燃机缸盖振动信号识别气缸压力的径向基函数 (radial basis function,RBF)神经网络方法。首先 ,给出了该方法的实现原理与步骤 ,并根据内燃机的工作特性 ,对径向基函数神经网络的参数进行了有效的设置 ,建立了完整的内燃机缸盖振动信号与气缸压力之间的非线性映射关系 ;然后 ,对试验数据进行了处理。结果表明 ,这种方法不仅在压力波形而且在特征点的数值上都具有较高的识别精度并有较强的鲁棒性。最后 ,对有关问题进行了讨论。

The core content of rough sets theory is introduced and the discrete method of continuous attribute value based on kohonen neural network is given.Rough sets theory is used to simplify attribute parameter reflecting operating conditions of diesel engine and in which unnecessary properties are eliminated.Fault diagnosis model and learning rule of RBF ANN is studied.Fault diagnosis principle and step of RBF ANN based on rough sets theory is given.Through automatic fault classification and diagnosis for plunger...

The core content of rough sets theory is introduced and the discrete method of continuous attribute value based on kohonen neural network is given.Rough sets theory is used to simplify attribute parameter reflecting operating conditions of diesel engine and in which unnecessary properties are eliminated.Fault diagnosis model and learning rule of RBF ANN is studied.Fault diagnosis principle and step of RBF ANN based on rough sets theory is given.Through automatic fault classification and diagnosis for plunger abrasion fault in fuel injection system of diesel engine,the example shows that this system reduces input node number and overcomes some shortcomings,such as neural network scale is too large and the rate of classification is slow.

介绍了粗糙集理论的核心内容 ,给出了基于 kohonen神经网络的连续属性值离散化方法。应用粗糙集理论对反映柴油机运行工况的特征参数进行了属性简化 ,剔除了不必要的属性。研究了 RBF神经网络故障诊断模型及学习规则 ,给出了基于粗糙集理论的 RBF神经网络故障诊断原理和步骤。通过对柴油机供油系统柱塞磨损故障的自动分类和诊断 ,表明该系统能有效地减少神经网络的输入节点数 ,克服了神经网络规模过于庞大及分类识别速度慢等缺点。

The measuring methods of coal load in ball mill tube of pulverizing system is analyzed, a soft sensing scheme is presented, which is based on multi parallel radial basis function (RBF) neural network, soft sensing system structure and algorithm are given. The soft sensor has been applied in field, and good performance has been validated, the results are satisfied.

分析了球磨机负荷测量的现状 ,提出了基于并行 RBF神经网络测量制粉系统球磨机磨筒内负荷的软测量方法 ,给出了相应的系统结构和算法。现场实测数据计算实例显示了该方法良好的测量性能

 
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