The paper presents an optimal control scheme based on moving optimization principle of model predictive control for linear system with constraints.
以预测控制的滚动优化原理为基础提出了一种约束线性系统的最优控制方法。
An iteration method is presented to solve the optimization function for approximating the rotation transformation part of the pseudo moving frame at any position of the motion trajectory.
采用迭代法求解该优化方程来逼近运动轨迹任意处伪活动标架的旋转变换矩阵。
The optimization problem can be solved based on the density-stiffness interpolation scheme and the method of moving asymptotes belonging to sequential convex programming approaches.
采用基于密度刚度插值模型和序列凸规划法中的移动渐近线方法求解优化模型。 通过经典算例验证了本方法的有效性。
Three search technologies which are called moving search, reducing search and speeding search are used in this optimization approach.
该优化方法主要采用移动搜索、收缩搜索和加速搜索三种搜索技术。
A new method of constructing compound CAM moving curve is proposed by taking some characteristic values as an optimization object.
提出一种以特性值为优化目标构造复合型凸轮运动曲线的新方法。
Using the rolling optimization concept adopted in predictive control, robot path planning in global unknown environment with moving obstacles is studied.
本文借鉴预测控制滚动优化原理，研究了全局环境未知且存在动态障碍物情况下的机器人路径规划问题。
A two level coordination algorithm is presented for moving horizon optimization of predictive control based on NARMA model.
针对NARMA模型，提出了预测控制滚动优化的两级协调法。
Chaotic mechanism is introduced to normal BP algorithm, and the problem of local limit value for network is solved using global moving characteristic of chaotic mechanism is weight optimization.
将混沌机制引入常规BP算法，利用混沌机制固有的全局游动，逃出权值优化过程中存在的局部极小点，解决了网络训练易陷入局部极小点的问题。
Particle swarm optimization (PSO) is proposed for detecting small moving target.
提出一种基于粒子群算法的运动小目标检测算法。
Particle swarm optimization (PSO) is proposed for detecting small moving target.
提出一种基于粒子群算法的运动小目标检测算法。
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