搜索资源列表
zcx
- 基于遗传算法的PID控制 用于优化求解控制器的参数 -based on genetic algorithms for optimizing PID control parameters Controller Solution
pid
- 人工智能（模糊在自动控制的应用）电动机控制中的简单遗传算法PID参数优化-Artificial intelligence (fuzzy in Automatic Control) motor control in the simple genetic algorithm optimization of PID parameters
gaPID
- 采用遗传算法优化传统PID控制，选取了合适的优化函数，采用实数编码-Genetic algorithm to optimize the traditional PID control, select the appropriate optimization function using real-coded
ACO-PID
- 除了蚁群算法，可用于PID参数优化的智能算法还有很多，比如遗传算法、模拟退火算法、粒子群算法、人工鱼群算法，等等。-In addition to the ant colony algorithm can be used to optimize the PID parameters, there are many intelligent algorithms, such as genetic algorithms, simulated annealing algorithm, particle s
ACO-PID
- 除了蚁群算法，可用于PID参数优化的智能算法还有很多，比如遗传算法、模拟退火算法、粒子群算法、人工鱼群算法，等等。-In addition to the ant colony algorithm, can be used in the intelligent algorithm of PID parameter optimization and there are many, such as genetic algorithm, simulated annealing algorithm, pa
PSO
- 粒子群优化算法与遗传算法结合，实现PID控制-PSO and genetic algorithm combined to achieve PID control
PID
- 应用多目标遗传算法优化对PID参数进行多目标优化-Multiobjective genetic algorithm optimization of multi-objective optimization PID parameters
beipao_v46
- 遗传算法无功优化，包括邓氏关联度、绝对关联度、斜率关联度、改进绝对关联度,IMC-PID是利用内模控制原理来对PID参数进行计算。- Genetic algorithm based reactive power optimization, Including Deng s correlation, absolute correlation, correlation of slope, improved absolute correlation, The IMC- PID is using the
peikou_v52
- IMC-PID是利用内模控制原理来对PID参数进行计算，遗传算法无功优化，有CDF三角函数曲线/三维曲线图。- The IMC- PID is using the internal model control principle for PID parameters is calculated, Genetic algorithm based reactive power optimization, There CDF trigonometric curve/3D graphs.
基于数控机床进给系统PID参数优化程序
- 本代码运行遗传算法对数控机床系统PID控制环节的参数进行了整定(This code runs the genetic algorithm(GA)to set the parameters of the PID control link of the CNC machine tool system.)
way_2
- 使用遗传算法对pid参数优化 遗传算法简称GA（genetic algorithms），它是模拟自然界遗传机制和生物进化论的一种并行随机搜索最优化方法。它将“优胜劣汰，适者生存”的生物进化理论引入优化参数形成的编码串联群体中，按所选择的适配值函数并通过遗传中的复制、交叉及变异对个体进行筛选，使适配值高的个体被保留下来，组成新的群体，周而复始，群体中个体适应度不断提高，直到满足某一条件。 。(Using genetic algorithm to optimize PID parameters)