Using Kullback-Leibler divergence to predict on artificial neural network

作     者：Ismail, I.A. Nabil, Tamer 

作者机构：Computer Science Dept. Faculty of Computer and Informatics Zagazig University Zagazig Egypt Basic Science Dept. Faculty of Computer and Informatics Suez Canal University Ismailia Egypt 

出 版 物：《Neural Network World》 (Neural Network World)

年 卷 期：2004年第14卷第6期

页      面：507-519页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Neural networks 

摘      要：Several algorithms have been developed for time series forecasting. In this paper, we develop a type of algorithm that makes use of the numerical methods for optimizing on objective function that is the Kullbak-Leibler divergence between the joint probability density function of a time series x1, x2,...,xn and the product of their marginal distributions. The Gram-charlier expansion is used for estimating these distributions. Using the weights that have been obtained by the neural network, and adding to them the Kullback-Leibler divergence of these weights, we obtain new weights that are used for forecasting the new value of xn+k.