NortonGoBack是完美工具，可用于处理随机系统崩溃、安装失败和无意删除的情况。
Norton GoBack is the perfect tool for random system crashes, failed installations, and inadvertent deletions.
技巧10：随机系统信息收集
地下水系统是一个复杂的随机系统。
地下水系统是一个复杂的随机系统。
最后讨论了随机系统参数递推估算的综合方法。
Finally, a synthesis method for recursive parameter estimation of a stochastic system is also discussed.
本文研究了模糊随机系统的模糊随机优化问题。
The optimization of fuzzy stochastic systems is studied in this paper.
应用神经网络对未建模型的非线性随机系统进行控制。
Use Neural Network to control unfounded model nonlinear random system.
本文将随机系统状态模型辨识技术用于电力系统负荷预报。
Power system load forecasting using stochastic system state model identification technique is proposed.
研究了马尔可夫跳变参数时滞随机系统的鲁棒保性能控制问题。
The problem of the robust guaranteed cost control for time-delay stochastic systems with Markov switching parameters is discussed.
鉴于含有加性噪声的指数模型描述了一类重要的非线性随机系统。
The exponential models with additive noise describe a class of important nonlinear stochastic systems.
本文讨论了奇异线性定常连续随机系统最小阶滤波器的设计问题。
In this paper, formulas are derived for the minimal order filter in a singular, linear time-invariant, continuous and stochastic system.
本文主要研究了随机系统中比较典型的一个问题：双态自适应控制。
This paper discusses a typical problem of stochastic systems: dual adaptive control.
本文提出了一种简化的多变量随机系统状态模型参数在线辨识方法。
A simplified on-line multivariable stochastic system state model parameters estimation algorithm is developed.
非线性随机系统的最优控制，采用基于性能势的随机优化数值算法。
The optimal control of nonlinear random system adopted random optimal numerical algorithm based on performance potential.
利用矩阵分解和系统变换的技巧，得到广义随机系统的集值滤波方程。
By using the techniques of the matrix decomposition and the system transformation, the set-valued filtering for stochastic singular linear systems is also designed.
针对线性随机系统提出了一种改进强跟踪卡尔曼滤波器(MSTKF)。
A modified strong tracking Kalman filter (MSTKF) for linear stochastic systems is proposed.
本文研究了线性离散随机系统的模型降阶和最优输出反馈矩阵的设计问题。
In this paper, the problems of the model reduction and the design of optimal output feedback matrix are discussed for linear stochastic discrete-time system.
卡尔曼滤波用于线性离散随机系统具有非常好的收敛性和滤除高频噪声的能力。
Kalman filter used in linear discrete stochastic system has good convergence and the ability to remove high frequency noises.
在概率论中，对非随机系统的随机影响的研究已经引起了越来越多的人的兴趣。
In the theory of probability, it has already aroused more and more many people's interest to research the influence of the non-stochastic system stochastic.
针对具有随机白噪声输入的随机系统，分析了PID控制下期望指标的相容性问题。
For the stochastic system with white noise input, the consistency problem of desired indices for PID controller is investigated.
利用广义逆理论和奇异值分解理论，研究离散型线性随机系统的综合控制设计问题。
This paper discusses the synthetical control designing problem for discrete linear stochastic systems with generalized inverse theory and the singular value decomposition theory.
当动力学模型存在未知的随机系统偏差时，两阶段卡尔曼滤波要优于标准卡尔曼滤波。
The two-stage Kalman filter is excellent than standard Kalman filter in the presence of unknown random bias.
本文采用随机系统识别方法，在模拟的风雨共同作用条件下识别了薄平板模型的气动导数。
In this paper, the flutter derivatives of a thin plate model under the simultaneous actions of wind and rain are identified by using the Covariance-Driven Stochastic Subspace Identification method.
提出了一类基于T - S模糊模型的非线性时滞随机系统均方镇定的LMI通用设计方法。
LMI design method for mean square stabilization of a class of nonlinear stochastic systems with time-delay based on the T-S fuzzy model is introduced.
提出一类基于T S模糊模型的非线性随机系统均方镇定的线性矩阵不等式(LMI)设计方法。
An LMI design method for mean square stabilization of a class of nonlinear stochastic systems based on the T-S fuzzy model is proposed.
在此基础上对随机系统、确定系统、混沌系统的复杂度进行了比较和讨论，并得出几点重要结论。
Then the comparison and discussion about the complexity of chaos and random systems and deterministic systems are conducted. Finally several important conclusions are given.
对于新型的复杂随机系统的预测方法的研究及随机系统预测的可靠性研究是预测工作者非常关注的。
Forecasting research workers show solicitude for the research of forecasting method and forecasted reliability in the stochastic system.
附加系统参数的混合平差模型中 ，同时含有非随机系统参数和具有验前统计性质的随机系统参数。
The mixing adjustment model with systematical parameters is an adjustment model including both non random systematical parameters and random systematical parameters.
附加系统参数的混合平差模型中 ，同时含有非随机系统参数和具有验前统计性质的随机系统参数。
The mixing adjustment model with systematical parameters is an adjustment model including both non random systematical parameters and random systematical parameters.
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