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.
出处
《电子与信息学报》
EI
CSCD
北大核心
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
关键词
信源
熵率
多重分形谱
隐马尔可夫过程
Information souee
Entropy rate
Multi-fractal
Hidden Markov processes
