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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...
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...
With the improvement of automobile electric degree, more and more people begin to pay attention to the fault diagnosis method and theories of electric controlled system. The precision and accuracy of on-board diagnosis methods, which with OBDII standard and has been widely used at present need to be further improvement. So, in this paper, take the engine idling instability as the example, put forward...
In management science, intelligent management that combines artificial intelligence with management science is an important research direction of management science. The efficiency question is the core of logistics system management, this paper analyses the operation efficiency of its every subsystem and set up index system of comprehensive evaluating logistics system efficiency (LSE). From the view...
Selective ensemble is effective for improve the classification performance through taking full advantage of the diversity and supplement between base classifiers. A BPSO (binary particle swarm optimization) based selective SVM ensemble approach is proposed to ensure the diversity and supplement among base classifiers in the training phase and high performance in the selection phase. Firstly, bootstrap...
This paper shows that share price could be confirmed accurately by the assessment model of stock price based on rough set (RS) and support vector machines (SVM). This model can remove the impact of "bull" and "bear" market and avoid controlling share price from executives effectively at the exercising date. According to the case analysis, the model is proved to be more exercisable,...
When applying traditional methods to train approximately linear support vector machine (SVM), we will get a kernel matrix which occupy mass computer memory and lead a slow convergence speed. In order to improve the convergence speed of SVM, a method of training approximately linear support vector machine based on variational inequality (VIALSVM) was proposed. The method turns the convex quadratic...
SVM which is based on statistical theory has the advantage of no relying on designer's experience of learning and the prior knowledge. So it is widely used in optimization, decision-making, regression estimates, speech recognition, facial image recognition, and so on. Because there are some kinds of wrong and isolated samples in the training samples in the forecasting model, and the learning process...
A face recognition method based on fuzzy data fusion is presented. In traditional principle component analysis method, operating directly on the whole face image leads to only global information about face image can be extracted and local one may be neglected. It is not very effective under variations of facial expression, pose and illumination. To solve this problem, in proposed scheme, each original...
In this paper, a robust and effective face detection method with HTF-Boosting is proposed. Firstly, a new feature, called Haar texture feature, is proposed that has many merits compared with Haar-Like feature. Secondly, a new Boosting algorithm, called Haar Texture Feature Boosting (HTF-Boosting), is proposed to construct strong face/nonface classifiers. The HTF-Boosting algorithm trains strong classifiers...
This paper introduces a novel Gabor-based Riemannian manifold learning (GRML) method for face recognition. Riemannian manifold learning (RML) is a recently proposed framework which formulates the dimensionality reduction of a set of unorganized data points as constructing normal coordinate charts for an underlying Riemannian Manifold. In this paper, we investigate its practical version for face recognition,which...
The basic principle of online Chinese signature identification is to compare one's signature characteristic value with those in the appointed pattern librarypsilas so that the signature to be identified is classified as either authentic or forged. In order to form a stable and effective pattern library, a new method is proposed based on the fact that the Fisher distinction criterion function is a...
It is known to all that obtaining an effectual feature representation is of paramount importance to face recognition. In this paper, the latest feature extraction method based on KCCA is introduced. However, in the training stage of the standard KCCA-based extractor, it requires to store and manipulate the kernel matrix, the size of which is square of the number of samples. When the sample numbers...
This paper describes a modified GMM based voice conversion method,in order to avoid the over-smoothed phenomena of traditional GMM conversion method, we adopt GMM method considering posterior threshold of each frame. We also considered the dynamic spectral features between frames to overcome the speech discontinuity problem,experiments show that our presented GMM method has shown better performance...
A robust inferential estimator model based on improved dynamic principal component analysis (DPCA) and multiple neural networks (MNN) was proposed. Data for building non-linear models was re-sampled using DPCA algorithm to form a number of sets of training and test data. For each data set, a neural network model was developed. To improve the robustness and accuracy of the neural networks, the MNN...
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