Robust Adaptive BP Algorithm And Its Application To The Stock Price Prediction

Based upon the deficiencies of the Back Propagation Algorithm in the practical application, after some mechanisms effecting the network training and the other performances are analyzed when training samples with disturbance are employed in training, in this paper, through combining the chief thoughts of the classical BP algorithm and the robust statistic technique, improving the optimal algorithm of the BP algorithm, A new algorithm with high robustness-Robust Adaptive BP algorithm is proposed, and also make a good effect when integrated this new algorithm with the dynamical BP network to predict the stock price.Compared with the classical BP algorithm, robust adaptive BP algorithm possesses some advantages as following:(1) Increasing the accuracy of the network training by means of using both the relative and absolute residual to adjust the weight values;(2) Improve the robustness and the network convergence rate through combining with the robust statistic technique by way of judging the values of the samples' relative residual to establish the energy function so that can suppress the effect on network training because of the samples with high noise disturbances;(3)Prevent entrapping into the local minima area and obtain the global optimal result owing to setting the learning rate to be the function of the errors and the error gradients when network is trained. The learning rate of the weights update change with the error values of the network adaptively so that can easily get rid of the disadvantage of the classical BP algorithm that is liable to entrap into the local minima areas.The simulation results of the new algorithm show that the robust adaptive BP algorithm is more efficient than others on the convergent rate, accuracy and especially on resisting to the noise effects.According to the characters of the stock market that possesses kinds of noises, serious nonlinear and difficult to model a precise mathematical equation, in this paper, applying the robust adaptive BP algorithm into the dynamical BP network to predict the stock price, one side can realize the course of having no use of modeling the accuracy model to the stock systems, and on the other hand can make good use of the strong robustness of the new algorithm result in conquer the influence of the noises to the network training and achieve the better prediction results.