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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.
      
The approximation properties of the RBF neural networks are investigated in this paper.
      
The main architectures, learning abilities and applications of radial basis function (RBF) neural networks are well documented.
      
Adapting RBF Neural Networks to Multi-Instance Learning
      
Nonlinear modeling based on RBF neural networks identification and adaptive fuzzy control of DMFC stack
      
The temperature models of anode and cathode of direct methanol fuel cell (DMFC) stack were established by using radial basis function (RBF) neural networks identification technique to deal with the modeling and control problem of DMFC stack.
      
Simulation results show that the RBF neural networks identification modeling method is correct, effective and the models established have good accuracy.
      
Three different classification frameworks have been studied:Multi-Layer Perceptron (MLP) Neural Networks, radial basis functions (RBF) Neural Networks, and Hidden Markov Model (HMM), and results of each framework have been reported and compared.
      
The paper first discusses bearing faults and Park's transform, and then gives a brief overview of the radial basis function (RBF) neural networks algorithm.
      
A new method of designing direct controllers of the PID type for nonlinear plants by using RBF neural networks is proposed, and its satisfactory performance is demonstrated through simulations.
      
On Solving the Inverse Scattering Problem with RBF Neural Networks: Noise-Free Case
      
The main goal of this study is the introduction and establishment of a new gap filling procedure using radial basis function (RBF) neural networks, which is also applicable under complex environmental conditions.
      
We applied adapted RBF neural networks within a combined modular expert system of neural networks as an innovative approach to fill data gaps in micrometeorological flux time series.
      
Classification of the Frequency of Carotid Artery Stenosis with MLP and RBF Neural Networks in Patients with Coroner Artery Dise
      
To be able to classify the data obtained from LICA and RICA in artificial intelligence, MLP and RBF neural networks were used.
      
Radial basis function RBF neural networks provide an attractive method for high dimensional nonparametric estimation for use in nonlinear control.
      
 

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