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fuzzyPID
- 某工业系统可等效为二阶环节带延迟...且执行机构具有0.07的死区和0.7的饱和区。采样时间T=0.01;系统的输入r(t)=1.5 要求1)设计一模糊控制器,使系统输出尽快跟随系统输入;2)模糊控制与PID控制的性能进行比较;3)系统有无延迟性能进行比较-An industrial system can be equivalent to second-order part of the implementing agencies with a delay ... and the dead
2
- matlab课程设计 课程报告题目:一类模糊PID控制器模糊查询表设计 2课程报告要求:按照讲课内容,在课程报告中应完整且详细地包括以下内容: 1设计任务 2确定输入量和控制量的论域及模糊参考集 3 确定模糊关系矩阵 4 输入量模糊化 5 模糊推理计算 6 模糊判决 7 模糊控制查询表 -matlab Course Design Course Report Title: a fuzzy PID controller, fuzzy look-up table d
rsar_1.3.3.tar
- sar is a Rough Set-based Attribute Reduction (aka Feature Selection) implementation. This is an implementation of ideas described, among other places, in the following paper: Qiang Shen and Alexios Chouchoulas, A Modular Approach to Generating Fu
FNNcontrol223
- Matlab实现模糊控制与传统PI控制比较后证明模糊控制修正优于PI控制( Input X: 2 membership functions, Y: 2 membership functions Output U: 3 membership functions)-Matlab fuzzy control with conventional PI control proved that the fuzzy control correction is superior to PI control (I
FNNcontrol335
- Matlab实现模糊控制与传统PI控制比较后证明模糊控制修正优于PI控制( Input X: 3 membership functions, Y: 3 membership functions Output U: 5 membership functions)-Matlab fuzzy control with conventional PI control proved that the fuzzy control correction is superior to PI control (I
fuzzy-control-table-line-calculation
- 离散论域模糊控制表的离线计算.已知单变量两维输入,一维输出模糊控制器,其两维输入为E,EC,一维输出为I,论域均为E,EC,I∈{-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6},E的论域划分为{NB,NM,NS,NZ,PZ,PS,PM,PB},共8个模糊子集-Discrete domain of fuzzy control table offline computing. Known univariate two-dimensional input, one-dimension
chap4
- .已知某炉温控制系统,要求温度保持在600℃恒定,针对该控制系统有以下控制经验: (1)若炉温低于600℃,则升压,低的越多升压越高; (2)若炉温高于600℃,则降压,高得越多降压越低; (3)若炉温等于600℃,则保持电压不变。 设模糊控制器为一维控制器,输入语言变量为误差,输出为控制电压。输入输出变量的量化等级为7级,取5个模糊集,试设计隶属度函数误差变化划分表,控制电压变化划分表和模糊控制规则表。 -Known for a temperature control sy
neural networks
- 1.elman神经网络对输入波形进行检测 2.设计具有3个神经元的Hopfield网络 3.建立自适应神经模糊推理系统对非线性函数进行逼近(正弦加滞后) 4.建立自适应神经模糊推理系统对非线性函数进行逼近(正弦多项式) 5.利用模糊C均值聚类方法将一类随机给定的三维数据分为三类(1.Detection of input waveform by elman neural network 2. design a Hopfield network with 3 neurons 3. est