参考资料：https: // blog.csdn.net / shelgi / article / details / 126908418
————通过下面这个例子，终于能理解一点模糊理论的应用了，感谢原作。
熟悉简单的skfuzzy构建接近生活事件的模糊控制器
假设下面这样的场景, 我们希望构建一套模糊控制系统, 通过室外温度和风的大小来判断穿几件衣服
室外温度的范围设置为0 - 40度, 虽然今年夏天超过40度在我们这边很平常, 但是我们这里还是以40度为最高界限
风的大小范围0 - 10, 这里不是风的级数, 而是我自己构建的大小.模糊理论奥妙就在于不需要精确的逻辑值,
可以模糊描述.比如小风我设置为1 - 3, 然后有点大的风等等, 都是比较抽象的描述, 但是经过隶属函数可以看出, 往往某个值是在多个状态叠加.
衣服的件数我设置为1 - 6(不能一件衣服不穿), 如果按照本人自己的爱好, 我最多也只穿三件.不过考虑到实际还是设一个大点的范围

常见模糊隶属度函数

import matplotlib.pyplot as plt
import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl
import matplotlib.pyplot as plt

"""
    scikit-fuzzy模块，它可以实现模糊控制系统
    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
    7.输出结果绘制结果3D图
"""

""" 方式一： 调用库函数 """
if 0:
    temp = ctrl.Antecedent(np.arange(0, 41, 1), 'temp')
    wind = ctrl.Antecedent(np.arange(0, 11, 1), 'wind')
    clothes = ctrl.Consequent(np.arange(1, 7, 1), 'clothes')

    # 自动找成员函数,分为三类
    temp.automf(3)
    wind.automf(3)

    # 设置目标的模糊规则
    clothes['low'] = fuzz.trimf(clothes.universe, [1, 1, 3])
    clothes['medium'] = fuzz.trimf(clothes.universe, [1, 3, 6])
    clothes['high'] = fuzz.trimf(clothes.universe, [3, 6, 6])

    rule1 = ctrl.Rule(temp['good'] | wind['poor'], clothes['low'])
    rule2 = ctrl.Rule(temp['average'], clothes['medium'])
    rule3 = ctrl.Rule(temp['poor'] | wind['good'], clothes['high'])

    rule1.view()
    rule2.view()
    rule3.view()

    # 创建控制系统,应用编写好的规则
    cloth_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])

    # 创建控制仿真器
    cloth_num = ctrl.ControlSystemSimulation(cloth_ctrl)

    # 输入测试数据
    cloth_num.input['temp'] = 20
    cloth_num.input['wind'] = 2

    # 设置去模糊方法
    clothes.defuzzify_method = 'mom'

    # 计算结果
    cloth_num.compute()

    cloth_num_res = cloth_num.output['clothes']
    print(f"The result of clothes: {cloth_num_res}")

    # 可视化
    clothes.view(sim=cloth_num)

    plt.show()

else:
    """ 方式二： 手动实现模糊规则 """
    plt.rcParams['font.family'] = 'simhei'
    x_temp = np.arange(0, 41, 1)
    x_wind = np.arange(0, 11, 1)
    x_clothes = np.arange(1, 7, 1)

    # 将三角隶属度函数对各个量进行隶属度映射
    temp_cold = fuzz.trimf(x_temp, [0, 0, 15])
    temp_warm = fuzz.trimf(x_temp, [5, 25, 35])
    temp_hot = fuzz.trimf(x_temp, [25, 40, 40])

    plt.figure()
    plt.title("Temperature")
    plt.plot(x_temp, temp_cold, 'b', label='cold')
    plt.plot(x_temp, temp_warm, 'y', label='warm')
    plt.plot(x_temp, temp_hot, 'r', label='hot')
    plt.legend()
    # plt.show()

    wind_low = fuzz.trimf(x_wind, [0, 0, 5])
    wind_medium = fuzz.trimf(x_wind, [0, 5, 10])
    wind_high = fuzz.trimf(x_wind, [5, 10, 10])

    plt.figure()
    plt.title("Wind")
    plt.plot(x_wind, wind_low, 'b', label='low')
    plt.plot(x_wind, wind_medium, 'y', label='medium')
    plt.plot(x_wind, wind_high, 'r', label='high')
    plt.legend()
    # plt.show()

    cloth_low = fuzz.trimf(x_clothes, [1, 1, 3])
    cloth_medium = fuzz.trimf(x_clothes, [1, 3, 6])
    cloth_high = fuzz.trimf(x_clothes, [3, 6, 6])

    plt.figure()
    plt.title("clothes")
    plt.plot(x_clothes, cloth_low, 'b', label='low')
    plt.plot(x_clothes, cloth_medium, 'y', label='medium')
    plt.plot(x_clothes, cloth_high, 'r', label='high')
    plt.legend()
    # plt.show()

    temp_test = 30
    wind_test = 5

    temp_level_cold = fuzz.interp_membership(x_temp, temp_cold, temp_test)
    temp_level_warm = fuzz.interp_membership(x_temp, temp_warm, temp_test)
    temp_level_hot = fuzz.interp_membership(x_temp, temp_hot, temp_test)

    wind_level_low = fuzz.interp_membership(x_wind, wind_low, wind_test)
    wind_level_medium = fuzz.interp_membership(x_wind, wind_medium, wind_test)
    wind_level_high = fuzz.interp_membership(x_wind, wind_high, wind_test)

    # 模糊规则
    # 当风小或者温度高的时候我们穿很少的衣服
    # 当温度中等, 比较温暖的时候我们穿得稍微多点
    # 当温度很低或者风很大的时候, 那我们就需要穿很多衣服了
    rule1 = np.fmax(temp_level_hot, wind_level_low)
    cloth_res_low = np.fmin(rule1, cloth_low)

    cloth_res_medium = np.fmin(temp_level_warm, cloth_medium)

    rule2 = np.fmax(temp_level_cold, wind_level_high)
    cloth_res_high = np.fmin(rule2, cloth_high)

    clothes = np.zeros_like(x_clothes)

    # vis
    plt.figure(figsize=(8, 3))
    plt.title("结果")
    plt.plot(x_clothes, cloth_low, 'b')
    plt.fill_between(x_clothes, 0, cloth_res_low)
    plt.plot(x_clothes, cloth_medium, 'g')
    plt.fill_between(x_clothes, 0, cloth_res_medium)
    plt.plot(x_clothes, cloth_high, 'r')
    plt.fill_between(x_clothes, 0, cloth_res_high)
    # plt.show()

    # 去模糊
    aggregated = np.fmax(cloth_res_low, np.fmax(cloth_res_medium, cloth_res_high))

    # 去模糊方法:
    # 反模糊化方法有很多
    # centroid面积重心法
    # bisector面积等分法
    # mom最大隶属度平均法
    # som最大隶属度取最小法
    # lom最大隶属度取最大法
    cloth = fuzz.defuzz(x_clothes, aggregated, 'mom')

    cloth_res = fuzz.interp_membership(x_clothes, aggregated, cloth)

    plt.figure(figsize=(8, 3))
    plt.title(f"去模糊化结果cloth:{cloth}")
    plt.plot(x_clothes, cloth_low, 'b')
    plt.plot(x_clothes, cloth_medium, 'g')
    plt.plot(x_clothes, cloth_high, 'r')
    plt.fill_between(x_clothes, 0, aggregated, facecolor='orange')
    plt.plot([cloth, cloth], [0, cloth_res], 'k')
    plt.show()

1. 测试温度：temp_test = 30；测试风速：wind_test = 5
   

2. 测试温度：temp_test = 10；测试风速：wind_test = 8
   

3. 测试温度：temp_test = 40；测试风速：wind_test = 2