一种新的基于伊藤随机微分方程的预测算法

基于随机过程概率密度函数的峰谷点、协同扩散过程的漂移系数、马尔可夫链“移向中心点”的性质及其状态转移值之间的关系，探讨了构造伊藤随机微分方程的马尔可夫链近似模型算法．仿真结果表明，不论是线性序列还是非线性序列，马尔可夫链近似模型算法预测误差的方差不仅远小于Burg格型预测器的方差而且近乎为常数．该算法的这些性质对预测编码十分有利．

A New Predictive Algorithm Based on the Ito Stochastic Differential Equation

Based on the relationship between the peak points and valley points of the probability density function of a stochastic process, the drift coefficient of its associated diffusion process, the shift back to center' property of the Markov chain and the state transitive value of the chain, the algorithm for constructing the approximating model of the Markov chain of an Ito stochastic differential equation is discussed. The simulation results demonstrate that the variance of the prediction error of the algorithm is not only much smaller than that of the Burg lattice predictor but also very close to constant whether the series is linear or not. These properties of the algorithm are beneficial to predictor and predictive coding.