Recently, many different types of artificial neural networks (ANNs) have been applied to forecast stock price and good performance is obtained. However, most of these models use only a small number of features as input and there may not be enough information to make prediction due to the complexity of stock market. If having a larger number of features, the run time of training would be increased and the generalization performance would be deteriorated due to the curse of dimension. Therefore, an effective tool to extract highly discriminative lowdimensional features from the high-dimensional raw input would be a great help in improving the generalization performance of the regression model. Restricted Boltzmann Machine (RBM) is a new type of machine learning tool with strong power of representation, which has been utilized as the feature extractor in a large variety of classification problems. In this paper, we use the RBM to extract discriminative low-dimensional features from raw data with dimension up to 324, and then use the extracted features as the input of Support Vector Machine (SVM) for regression. Experimental results indicate that our approach for stock price prediction has great improvement in terms of low forecasting errors compared with SVM using raw data.
An analytic flying model that can well represent the physical behavior is derived, where the ball's self-rotational velocity changes along with the flying velocity. Based on the least square method, a rebound model that represents the relation between the velocities before and after rebound is established. The initial trajectory is fitted to three second order polynomials of the flying time with the measured positions of the ball. The initial velocities of the ball in the analytic flying model, including the flying velocity and the self-rotational velocity, are computed from the polynomials. The ball's landing position and velocity is predicted with the model. The velocities after rebound are determined with the rebound model. By taking the velocities after rebound as new initial ones, the flying trajectory after rebound is described with the model again. In other words, the ball's trajectory is predicted. Experimental results verify the effectiveness of the proposed method.Index Terms-Trajectory prediction, spinning ball, flying modeling, rebounding modeling, least square method, ping-pong player robot.
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