Python模糊控制库使用

模糊控制库安装

模糊控制库github：链接: Github.
python安装：pip install scikit-fuzzy

示例

1.选择输入输出模糊集
2.定义输入输出隶属度函数（不同的隶属度函数，会导致不同的控制特性）
3.建立模糊控制表
4.建立模糊控制规则
5.模糊推理
6.反模糊化
例子：输入为服务(service)和质量(quality)两个参数，输出为得到的小费(tip)

定义模糊控制变量

1.定义模糊控制变量，质量和服务的取值范围为[0，10]，小费范围为[0，25]

x_qual = np.arange(0, 11, 1)
x_serv = np.arange(0, 11, 1)
x_tip  = np.arange(0, 26, 1)
quality = ctrl.Antecedent(x_qual, 'quality')
service = ctrl.Antecedent(x_serv, 'service')
tip = ctrl.Consequent(x_tip, 'tip')

模糊隶属函数

2.生成模糊隶属函数，这里是三角形隶属度函数

#定义质量差时的三角隶属度函数横坐标
quality['L'] = fuzz.trimf(x_qual, [0, 0, 5])  
quality['M'] = fuzz.trimf(x_qual, [0, 5, 10])
quality['H'] = fuzz.trimf(x_qual, [5, 10, 10])
#定义服务差时的三角隶属度函数横坐标
service['L'] = fuzz.trimf(x_serv, [0, 0, 5]) 
service['M'] = fuzz.trimf(x_serv, [0, 5, 10])
service['H'] = fuzz.trimf(x_serv, [5, 10, 10])
#定义小费的三角隶属度函数横坐标
tip['L'] = fuzz.trimf(x_tip, [0, 0, 13]) 
tip['M'] = fuzz.trimf(x_tip, [0, 13, 25])
tip['H'] = fuzz.trimf(x_tip, [13, 25, 25])
#解模糊方法采用质心法
tip.defuzzify_method='centroid'
#可视化这些输入输出和隶属函数
# quality.automf(3) #自动分配几种程度NS ZO PS
#service.automf(3)
#quality.view()
# service.view()
# plt.show()

模糊规则

3.制定模糊规则，即服务，质量和小费的关系

rule1=ctrl.Rule(antecedent=((quality['L'] & service['L'])|(quality['L'] & service['M'])|(quality['M'] & service['L'])),consequent=tip['L'],label='Low')
rule2=ctrl.Rule(antecedent=((quality['M']&service['M'])|(quality['L']&service['H'])|(quality['H']&service['L'])),consequent=tip['M'],label='Medium')
rule3=ctrl.Rule(antecedent=((quality['M']&service['H'])|(quality['H']&service['M'])|(quality['H']&service['H'])),consequent=tip['H'],label='High')
#rule2.view() #这个图我没看懂，有哪位大哥看懂了可以告知一下

激活模糊控制器

4.激活模糊控制器，输入质量值6.5，服务值为9.8时，输出小费的值，并绘制结果

tipping_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])
tipping = ctrl.ControlSystemSimulation(tipping_ctrl)
tipping.input['quality'] = 6.5
tipping.input['service'] = 9.8
tipping.compute()
print (tipping.output['tip'])
tip.view(sim=tipping)
plt.show()

完整代码

import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt
from skfuzzy import control as ctrl
import math
#  质量和服务范围为[0，10]
#  小费范围为[0，25]
x_qual = np.arange(0, 11, 1)
x_serv = np.arange(0, 11, 1)
x_tip  = np.arange(0, 26, 1)
# 定义模糊控制变量
quality = ctrl.Antecedent(x_qual, 'quality')
service = ctrl.Antecedent(x_serv, 'service')
tip = ctrl.Consequent(x_tip, 'tip')
# 生成模糊隶属函数
quality['L'] = fuzz.trimf(x_qual, [0, 0, 5])  #定义质量差时的三角隶属度函数横坐标
quality['M'] = fuzz.trimf(x_qual, [0, 5, 10])
quality['H'] = fuzz.trimf(x_qual, [5, 10, 10])
service['L'] = fuzz.trimf(x_serv, [0, 0, 5]) #定义服务差时的三角隶属度函数横坐标
service['M'] = fuzz.trimf(x_serv, [0, 5, 10])
service['H'] = fuzz.trimf(x_serv, [5, 10, 10])
tip['L'] = fuzz.trimf(x_tip, [0, 0, 13]) #定义小费的三角隶属度函数横坐标
tip['M'] = fuzz.trimf(x_tip, [0, 13, 25])
tip['H'] = fuzz.trimf(x_tip, [13, 25, 25])

tip.defuzzify_method='centroid'
#可视化这些输入输出和隶属函数
# quality.automf(3)
#service.automf(3)#三种程度
#quality.view()
# service.view()
# plt.show()
#规则
rule1=ctrl.Rule(antecedent=((quality['L'] & service['L'])|(quality['L'] & service['M'])|(quality['M'] & service['L'])),consequent=tip['L'],label='Low')
rule2=ctrl.Rule(antecedent=((quality['M']&service['M'])|(quality['L']&service['H'])|(quality['H']&service['L'])),consequent=tip['M'],label='Medium')
rule3=ctrl.Rule(antecedent=((quality['M']&service['H'])|(quality['H']&service['M'])|(quality['H']&service['H'])),consequent=tip['H'],label='High')

rule2.view()
tipping_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])
tipping = ctrl.ControlSystemSimulation(tipping_ctrl)
tipping.input['quality'] = 6.5
tipping.input['service'] = 9.8
tipping.compute()
print (tipping.output['tip'])
tip.view(sim=tipping)
plt.show()

附上模糊控制库网站，有更详细说明和例子：
链接: 模糊控制网站.

本文地址：https://blog.csdn.net/qq_39330520/article/details/107298819