用统计物理的方法计算信源熵率  

Computing the Entropy Rate of Information Source with Methods of Statistical Physics

作　　者：陈双平[1] 郑浩然[2] 马猛[2] 张振亚[1] 王煦法[2] 

机构地区：[1]中国科学技术大学电子工程与信息科学系,合肥230027 [2]中国科学技术大学计算机科学与技术系,合肥230027

出　　处：《电子与信息学报》2007年第1期129-132,共4页Journal of Electronics & Information Technology

基　　金：中国科技大学高水平大学建设重点项目;中国科学技术大学青年基金资助课题

摘　　要：从数学模型的角度来说,信源和随机过程有着一一对应的关系。从混沌的角度来看,随机过程的多重分形谱是动力系统的重要特征,熵率只是多重分形维中特殊的一维,即信息维。该文指出了如何用统计物理的方法计算随机过程的多重分形维,以二态隐马尔可夫信源作为例子,该文计算了其熵率。计算结果和理论结果的比较表明,用统计物理的方法计算隐马尔可夫过程熵率具有实用价值。这一方法可以推广到一般信源熵率的数值计算。From the mathematical point of view, information sources can be 1-to-1 mapped to stochastic processes. Known from the theory of chaos, multi-fractal of stochastic process is a key characteristic of its dynamics, of which entropy rate is a special fractal dimension named information dimension. The paper introduces methods of statistical physics to compute the multi-fractal of stochastic process so that the entropy rate of source can be obtained at once. Take binary hidden Markov processes as example, the paper demonstrate how this approach works. The results shows that the methods is applicable to numerically approximate the entropy rate of binary hidden Markov processes （BHMPs） in practical applications, and it can be applied in more generalized kinds of information sources.

关 键 词：信源 熵率 多重分形谱 隐马尔可夫过程 

分 类 号：TN911.2[电子电信—通信与信息系统]