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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 studies target recognition for high resolution radar by range profile. Three methods of recognition are tested by using an experiment ISAR data,which are radial basis function (RBF) neural network method in frequency domain and two correlation filters (one in space domain and another in frequency domain).

本文研究基于一维距离像的高分辨雷达目标识别方法。文中讨论了三种识别方法(频率域RBF神经网络方法、频率域相关滤波器和距离域相关滤波器),并用我国实验ISAR录取的数据比较了这三种方法在不同信噪比下的识别性能。

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。

Presents a new hybrid framework of hidden Markov models (HMM) and radial basis function (RBF) neural networks for speech recognition. Here, the HMM is employed to produce a best speech state sequence which is warped to a fixed dimension vector and the RBF neural network is used as classifier. The theoretical analysis and experimental results show that the new hybrid system works better than HMM especially in recognition of highly confusable words.

提出了一种隐马尔可夫模型(HMM)和径向基函数神经网络(RBF)相结合的语音识别新方法。该方法首先利用HMM生成最佳语音状态序列,然后用函数逼近技术产生对最佳状态序列进行时间规正,最后通过RBF神经网络进行分类识别。理论和实验结果表明,该系统比HMM具有更好的识别效果,特别对提高易混淆词的识别性能尤为显著。

 
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