stochastic process bound 随机过程界限
Compared with the traditional method, it does not need disassembling the power spectral densities of stochastic signal covariance and is not bound to if the power spectral densities are rational.
与传统的方法相比，无需对随机信号的协方差函数的功率谱进行分解，不受限于协方差函数的功率谱是否为有理式。
Based on stochastic process theory, the bounded convergence of forgetting factor least square algorithm (FFLS for short) is studied and the upper bound of the parameter tracking error is given.
利用随机过程理论研究了遗忘因子最小二乘法(FFLS)的有界收敛性，给出了参数估计误差的上界。
The differential and integral equation for survival probability and a upper bound of ruin probability are given by using renewal theory and stochastic process approach.
利用更新理论和随机过程等方法，给出了模型生存概率所满足的微积分方程关系式和破产概率的一个上界估计。
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