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rbf神经网络     
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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


In this paper, a one-step ahead predictive control algorithm based on Radial Base Function (RBF)neural networks is proposed, which needs only one neural network. The algorithm is simple, and the control value can be acquired by only a few iterations. Thus it has good real-time property. Through the simulation for a discrete nonlinear system, it is obvious that the algorithm posseses good control performance such as robustness.

提出一种基于径向基函数(RBF)神经网络的一步超前预测控制算法。该方法只用一个网络,控制量的获取只求几步迭代,算法简单并有较好的实时性。通过对离散非线性系统的仿真证明了算法的有效性.

This paper proposes a new method for the training of Radial Basis Function (RBF) neural networks, which is a compound algorithm of Gauss-Jordan algorithm and General Inverse algorithm. The simulating results show that the training of the method is very quickly convergent, a good real-time way and has better quality than Orthogonal Least Squares Algothrism in Least Mean Square error and convergence.

提出了一种用于径向基函数(RBF)神经网络训练的新方法,即Gauss-Jordan与求广义逆(genemalinverse)的复合法。仿真结果表明,此方法训练速度快,实时性强,其收敛性和收敛精度均比正交最小二乘算法(OLS)效果好。

Radial basis function neural networks is very useful in nonlinear system modeling. In this paperthe radial basis function neural network has been analyzed and improved. Simulation shows that the improvedneural network has successfully modelled the continuous stirred tank reactor system.

本文从径向基函数(RBF)神经网络的特点着手,分析了该网络存在的问题,并且对网络径向基函数中心的选取、计算以及网络的拓扑结构作了改进,最后用改进的径向基函数神经元网络对化工中的连续搅拌反应釜(CSTR)系统进行建模,结果表明方法有效.

 
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