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The following topics are dealt with: neural networks; soft computing; intelligent control; intelligent systems; machine learning; pattern recognition; human-computer interaction; information security; information processing; wireless communication; circuits and systems; industrial applications; management science and engineering; intelligent computing; and mobile computing and networks.
This paper has analysed the a priori algorithm performance, and has pointed out performance bottleneck question of the a priori algorithm. Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy respectively. There is a paradox between efficiency and accuracy. In order to resolve to this conflict, a novel algorithm based on probability estimate...
Support vector machine (SVM) has been a promising method for data mining and machine learning in recent years. However, the training complexity of SVM is highly dependent on the size of a data set. A cluster support vector machines (C-SVM) method for large-scale data set classification is presented to accelerate the training speed. By calculating cluster mirror radius ratio and representative sample...
An online identification approach for boundary condition identification of fluid-filled piping systems is developed. Considering the lateral vibration of the fluid-filled pipes, the method combines the traveling wave method and the BP (backpropagation) neural network to estimate the boundary parameters. The traveling wave method is used to generate the training samples that contain several lower natural...
Based on analyzing fundamental principle of back propagation network model, the paper has established a topology network structure include 12 input layer 25 hidden layer and 2 output layer, 12 input nodes correspond the heighten expression of well performance time cell, 2 output nodes correspond the crude output and water production. According to the tracking model of BP network, this paper takes...
A method based on the neural network to predict the strains of the gas generator in a liquid rocket engine is presented for the fault analysis of the gas generator. A modified back-propagation algorithm is proposed to train the neural network. The training and testing samples are generated with an experiment. In the experiment, four strains in the risk domain of the gas generator and three forced...
In this paper, a new method was introduced in the Chinese license plate recognition. We propose a convolutional neural network architecture designed to recognize license plate directly from pixel images with no preprocessing. We present the image transformation applied on the original license plate to increase the training database. We also provide experimental results to demonstrate the robustness...
The traditional prediction model is not able to achieve a satisfying prediction effect in the problem of a non-linear system and nonstationary financial signal. The existing wavelet neural network has overcome the deficiency of traditional prediction model which is limited to linear system when predicting. However, wavelet neural network has a defect of confusing signal frequency. Based on the theory...
Data mining techniques, especially classification methods, are receiving increasing attention from researchers and practitioners in the domain of petroleum exploration and production (E&P) in China. To extensively investigate the effects of feature selection and learning algorithms on the hydrocarbon reservoir prediction performance, taking three real-world multiclass problems as examples, namely...
Two-dimensional principal component analysis technique is an important and well-developed area of image recognition and to date this method has been put forward. A new face recognition method two-dimensional principal component analysis (2DPCA) based on BP neural networks, named 2DPCA-BP method, was proposed. 2DPCA was used to obtain a family of projected feature vectors, in which face image was projected...
In this paper a novel method for subspace decomposition and its dimension estimation based on principle components analysis (PCA) neural network is proposed. This method use an improved Sanger PCA network model which can directly process the array data to obtain its signal subspace and does not involve any estimation of the covariance matrix or its Eigen decomposition. Meanwhile, this method can estimate...
The 3dB beamwidth of the DOA estimation method based on the singular value decomposition of the signal phase matching principle (SVDSPM) was about 1/3 to 1/2 as much as that obtained by MUSIC at different SNR. However, the SVDSPM algorithm searched the optimal solutions with certain frequency, and the complexity and computational load of optimizing the variables prevented it from applications in the...
This paper proposes an improved algorithm to optimize the fitness function, with which the iteration times can be efficiently reduced in simulating fabric pattern deformation when it flags in the wind. Relying on a genetic algorithm model used to its deformation, an iteration method is employed and performed when genes are substituted with positions and color values of each point on the fabric surface...
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