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This paper describes a new method for sensors multiple fault diagnosis and isolation. The information fusion method is based on expanded evidence theory, which offers a new combination rule under different but compatible frames of discernment. By this method, the maximum of available knowledge supported by each source of information is exploited and the uncertainty of the effective state between the...
RBF, as a feedforward neural network with single hidden layer, is applied widely in signal disposing, system modeling, control fields, etc. But the decision of its structure lacks effective methods. The discussion on ability of network generalization ability is one of important research aspects. The paper proposed a method based on PCA to decide the number of hidden neurons. Firstly it gives the larger...
This paper presents an improved adaptive radial basis function neural network (RBF NN) for nonlinear and nonstationary signal. The proposed method possesses distinctive properties of Lyapunov theory-based adaptive filtering (LAF) in Seng Kah Phooi et. al, (2002). This method is different from many RBF NN training methods using gradient search techniques. A new Lyapunov function of the error between...
A novel model for independent radial basis function (IRBF) neural network employing Gabor-based kernel PCA with fractional power polynomial models for feature extraction is proposed in this paper. In the new model, a bank of Gabor filters is first built to extract Gabor face representations characterized by selected frequency, locality and orientation to cope with various illuminations, facial expression...
In this paper, a color pattern recognition technique that is suitable for multicolor images of bark has been analyzed and evaluated. To extract the bark texture features, Gabor filter the image has been filtered with four orientations and six scales filters, and then the mean and standard deviation of the image output are computed. In addition, the obtained Gabor feature vectors are fed up into radial...
The paper uses the learning algorithm of support vector machine to separate both 106 listed companies of China in 2000 and 80 borrowers of a national commercial bank of China in 2001 into two patterns respectively by using two different kernel functions: polynomial function and radial basis function. The experimental results show that, under the circumstance of LIBSVM, the learning algorithms of support...
In order to control the turbojet engine in the whole flight envelope, this paper will establish the self-adaptive PID neural network control on the basis of the combination of the identification network of the RBF neural network and the controller of the BP neural network. RBF neural network adopts the offline training and the on-line adaptation of weight and bias. To speed the convergence, it will...
Radial basis function (RBF) neural network can be used as a universal approximator. In this paper, we propose a novel method to apply RBF net to reconstruct 2-dimensional computerized tomography (CT) images from a small amount of projection data. In the method, the cross-sectional image is represented by a RBF network, the unknown cross-sectional image vector is replaced by the function of the network's...
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