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 tensileshear 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 GARBF neural network identification model.


A Deep Web query interfaces classification method based on RBF neural network


The model contains two parts: first, fuzzy mathematics theory is used to standardize the samples; second, the RBF neural network and the BP neural network are used to train the standardized samples.


The evaluation result was compared with that of the RBF neural network method and the reported results in the Shaoguan area in 2005.


A radial basis function (RBF) neural network model was trained by the inputoutput data of impedance.


The RBF neural network model was used to test the fuzzy neural network controller.


Nuclear power plant fault diagnosis based on geneticRBF neural network


As a result, GeneticRBF Neural Network (GRBFNN) makes the neural network smaller in size and higher in generalization ability.


Identification of TSS in the human genome based on a RBF neural network


A PeopleCounting System Using a Hybrid RBF Neural Network


A peoplecounting system using hybrid RBF neural network is described.


In this paper, with the RBF neural network's ability of identifying complex nonlinear systems, a neural network identification model of MCFC stack is developed based on the sampled inputoutput data.


Linguistic Rule Extraction From a Simplified RBF Neural Network


Prediction of toxicity using a novel RBF neural network training methodology


A threelayer radial basis function (RBF) neural network is designed, which solves the nonlinear GPS pseudorange equations directly as opposed to the linear least squares or extended Kalman filter approaches in traditional GPS receivers.


These data were used in the training of a multilayer perceptron (MLP) and radial basis function (RBF) neural network to develop a classification mechanism capable of distinguishing between different heart sound abnormalities.

