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随机动态模型

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A stochastic nonlinear programming model of production with delay interaction (which is a static optimization model to solve the dynamic optimization problem) is built up and transformed into a deterministic nonlinear programming model and further into a linear programming model by adding constraints.

首先根据实际需要建立关联方程有延迟的车间生产的随机非线性规划模型,即一种求解动态优化问题的静态优化模型。

Further, applying the stochastic control theory and differential game, we discuss the dynamic pricing strategy in competitive market, get the optimum solution, and compare the revenue in competitive market with the revenue in monopolistic market.

进一步应用随机控制理论,建立了竞争市场下的动态定价博弈模型,提出了两种竞争策略,比较了这两种竞争策略和垄断市场下的收入。

In the scheduling layer, we discussed such papermaking workshop which has stochastic demand and failure. Mathematical model is setup. This problem are inverted into discrete dynamic programming.

在调度层针对具有随机需求和纸机故障的造纸车间,建立了数学模型,并利用一致化转移方法将其转化为离散动态规划问题。

Owing to the complexity of CIMS' missions,the reliability modeling should adapt the production processes accurately.Conventional reliability model method can not reflect the time dynamic behavior of CIMS,so the multitask reliability model based on generalized stochastic Petri nets is established for a CIMS manufacturing shop with multiple missions and the process times of the machines with exponential distributions.Furthermore,analysis of GSPN model based on behavior expression and moment generating function is presented,then can compute the transfer functions.Finally,the multitask reliability can be easily obtained without the reachability graph of Petri nets,which measure can reflect the operating performance of the whole multitask CIMS more intuitively.

CIMS生产的复杂性要求其可靠性模型可以精确地反映生产过程,由于传统的可靠性建模方法无法兼顾CIMS的时间动态特性,所以针对一个复杂的有多个加工任务的CIMS制造单元,由于其机器加工工件的时间均为指数分布,则采用广义随机Petri网进行多任务可靠性建模,在此基础上基于Petri网行为表达式,将矩母函数思想引入其中,不必生成可达标识图就可通过计算模型的传递函数,进而得到整个系统的多任务可靠度,该可靠性指标可以更直观地反映具有多种加工任务的整个CIMS的运行性能。

For GPS precise kinematic/mobile positioning, the function models and stochastic models in both single epoch way and OTF (on-the-flying) way are respectively given, and the general normal equations to suit programming in computer are constructed in this section.

对于 GPS 精密动态定位,分别给出了在单历元方式和 OTF 方式下的函数模型和随机模型,以及适宜于编程的通用法方程式。

Then by utilizing the features of this model an online optimization algorithm that combines policy gradient estimation and stochastic approximation is derived.

利用此模型的动态结构特性,结合在线学习估计梯度与随机逼近改进策略,提出动态电源管理策略的在线优化算法。

Based on the principle of Markov process and multiobjective programming, a stochastic multiobjective dynamic programming model considering faced runoff prediction is developed for the optimization of generation and water supply at Danjiangkou reservoir. The reservoir will be used for water supply of the south-north water transfer project.

本文通过对丹江口水库综合利用任务的分析,运用随机规划与多目标规划的基本理论和方法,建立了考虑面临时径流预报下丹江口水库发电及向外流域供水的随机多目标动态规划模型,并分析了长期平稳运行特性。

At last, we discuss the computational problems in solving the model. After an introduction of some common used algorithms and an analysis on their advantages and shortcomings, a new method for solving stochastic programrning stochastic dual dynamic programming is presented.

本章最后讨论了模型求解问题,在介绍常用随机规划算法和分析它们优缺点的基础上,给出一种新的求解方法一随机对偶动态规划算法。

The first one focuses upon the fundamental theory and methods of system identification, including the basic concepts of system description and identification,review of matrix theory, stochastic process and pulse response of linear systems; the first principle models for lumped and distributed parameter systems; modeling of bioreactions; auto regressive moving average model; weighted least square and regressive weighted least square for parameter estimation of ARMA model; Runge-Kutta and Simplex methods; an example of BPNN application; as well as Kalman Filter.

第一部分包括知识驱动建模、数据驱动建模、参数估计和状态估计的基础理论和方法。具体为:(1)系统辨识和系统描述的基本概念,包括矩阵论、随机过程、线性系统的脉冲响应函数等基础知识回顾;(2)基于动态物料和能量衡算的集总参数、分布参数对象的知识驱动建模方法;(3)生物反应系统的建模方法;(4)自回归移动平均模型;(5) ARMAX模型参数的加权最小二乘参数估计一次性算法和递推算法,相应的统计性质;(6)龙格库塔法和单纯形法;BP神经网络应用实例;(7)卡尔曼滤波器,及其与加权最小二乘参数估计递推算法的对比。

Considering this, it is necessary to adopt a stochastic model to describe an engineering dynamic system.

当对系统研究有较高的精度要求时,充分考虑随机因素的影响就必须用随机模型来描述系统的动态规律。

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