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In order to effectively predict the stock price in Chinese security market, this study establishes GM and SVM model; it analyzes the influence of financial indicators on listed companies and compares different stock price forecast methods. The research takes Chinese listed companies in shanghai A-stock market as sample, pretreats and introduces the financial indicators into the models, and finally...
RF power amplifiers (PA) are a major source of nonlinearity in a communication system. Accurate behavioral models are indispensable for PA linearization. To describe nonlinear characteristics of power amplifiers, a support vector machine (SVM) based modeling method is presented. The kernel approach and duality theory are employed to train the PA model. Simulation results show that the proposed model...
Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and to construct protein interaction networks, the application of most existing methods is limited because of less and incomplete information. In the present work, we integrate multi-databases which contain protein information and apply...
There is much difficulty in fault diagnosis because of lacking of system fault samples. So this paper presents a mixed strategy of combining differential evolution algorithm with local enhanced operator with the optimization of the parameters of support vector machine. For the diesel engine valve clearance fault diagnosis, the measured diesel engine valve vibration signals data after wavelet transform...
This study is conducted to present an application of support vector machine (SVM) method and image processing techniques for corn/weed seedlings in the fields. The original images obtained from the field are used to be preprocessed by a space transform and image processing techniques at first. Corn seedlings or small weeds are segmented by H channel using OTSU method. We found that H channel is better...
Choose of training samples is an important topic for the support vector machines applied research. This paper proposes a new image segmentation method: the two-dimensional histogram of the segmented image is used to guide to select training samples to train the classical support vector machines. First of all, calculate the two-dimensional histogram of the segmented image and then get its two thresholds...
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