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This study presents a hybrid data mining model for stock price that combines wavelet transform and support vector machines. In our proposed method, the wavelet transform is firstly applied to eliminate the noise of the stock time series. Secondly, the stepwise regression is employed in feature selection. Thirdly, the time delay concept is utilized to obtain the optimum prediction model. For illustration...
The paper described a structure of three modules diagnosis system for detecting and identifying faults that occur in the sensor and actuator of control systems with input and output signals related to the component itself. The diagnosis algorithm consists of three steps; firstly, generalized morphological filter with multi-structure elements is designed to filter the random noise and impulse noise...
In order to reduce the underwater ultrasonic image speckles and to enhance the image characteristics, an anisotropic diffusion model is established based on the wavelet transform in this paper. At the same time, it also provides the steps of the algorithmic architecture and analyzes the key links of the model during its application. Two-check experiments have been done using simulation data and real...
A novel semi-blind defocused image deconvolution technique is proposed, which is based on RBF neural network and iterative Wiener filtering. In this technique, firstly a RBF neural network is trained in wavelet domain to estimate defocus parameter. After obtaining the point spread function (PSF) parameter, iterative Wiener filter is adopted to complete the restoration. We experimentally illustrate...
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