Abstract. Stock forecasting technology has become a hot spot for scholars and investors, with its unique charm to attract a large number of scholars and investors to be committed to the stock market forecast research. Particularly, intelligent forecasting technology has made significant achievements in this regard. This paper describes 14 advanced neural networks and support vector machine forecasting techniques at home and abroad, analyzes and summarizes the characteristics and key points of each forecasting method. Finally, it puts forward the existing problems of stock forecasting research methods and explores the prospect of the future.
In order to improve the problem that the traditional PID control oven controlled crystal oscillator cannot be adjusted in real time in the process of clock taming, a BP neural network tuning PID control algorithm is proposed. BP neural network tuning PID control algorithm can learn the rule of PID control online, and can adjust the parameters of PID control in real time. The results of simulation in MATLAB show that there is no obvious overshoot and oscillation in step response controlled by BP neural network tuning PID, and the system is stable faster than the traditional PID control. Therefore, BP neural network tuning PID control has better control effect than traditional PID control in oven controlled crystal oscillator control, and has a strong self-adaptability in parameter adjustment.
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