搜索资源列表
PID-GAs
- 遗传算法的PID调节 题目：已知 ，利用GA 寻优PID参数，其中K=1，T=2， ，二进制/实数编码，位数不限，M，Pc，Pm自选，性能指标 ，Q=100为仿真计算步长。-PID regulation of genetic algorithms Title: known, the use of PID parameters of GA optimization, in which K = 1, T = 2,, binary/real-coded, not limited to the me
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
Genetic-algorithm--of-PID
- 用遗传算法来计算PID参数，使得PID参数能够在线调整-Genetic algorithm is used to calculate the PID parameters, the PID parameters can be adjusted online
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
intelligent-control
- 文件包含遗传算法，神经网络算法仿真，模糊PID算法等仿真，仿真结果电子文档，分析详细，所得结果都有截图。-Files contain genetic algorithms, neural network algorithm simulation, fuzzy PID algorithm simulation results of electronic documents, detailed analysis, the results have screenshots.
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
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.
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)