Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks

:Machine Learning(ML)algorithms have been widely used for financial time series prediction and trading through bots.In this work,we propose a Predictive Error Compensated Wavelet Neural Network(PEC-WNN)ML model that improves the prediction of next day closing prices.In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs.An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence.The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange.The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs.The RMSE error is 33%improved when the proposed PEC-WNN model is used compared to the Long ShortTerm Memory(LSTM)model.Furthermore,through the analysis of training mechanisms,we found that using the updated training the performance of the proposed model is improved.The contribution of this study is the applicability of simultaneously different time frames as inputs.Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.

Space radiation effect on fibre optical gyroscope control circuit and compensation algorithm

The process of a γ-irradiation experiment of fibre optical gyroscope （FOG） control circuit was described, in which it is demonstrated that the FOG control circuit, except for D/A converter, could endure the dose of 10krad with the protection of cabin material. The distortion and drift in D/A converter due to radiation, which affect the performance of FOG seriously, was indicated based on the elemental analysis. Finally, a compensation network based on adaptive neuro-fuzzy inference system is proposed and its function is verified by simulation.

Study on Adaptive Control with Neural Network Compensation

A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify parameters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.

RESEARCH ON CONTROL METHOD OF HYDRAULIC TORQUE SIMULATOR

An effective controller and compensator is designed by using the system identification and constant structure theory to realize the effective control. The experimental results indicate the extraneous torque can be decreased by 90% and the characteristics can be improved greatly by means of this kind of method.

Technologies for Compensation or Mitigation of Transmission Impairments in Future High-Speed Optical Networks

The presentation will give an overview over different classes of signal impairments in ultra-long-haul and high-speed optical WDM transmission systems and adequate approaches for suppression, mitigation or compensation are discussed.

Application of Automatic Chromatic Dispersion Compensation for Realization of All-Optical Network

We have studied the algorithm for the automatic chromatic dispersion compensation using bit error rate (BER) and Q-factor optimization for realization of dynamically reconfigurable all-optical .network. We have made sure good performance using the compensation system by laboratory experiments.