【人工智能】实验二: 洗衣机模糊推理系统实验与基础知识

04-27 1324阅读 0评论

实验二: 洗衣机模糊推理系统实验

实验目的

理解模糊逻辑推理的原理及特点，熟练应用模糊推理。

实验内容

设计洗衣机洗涤时间的模糊控制。

实验要求

已知人的操作经验为：

  • “污泥越多，油脂越多，洗涤时间越长”；
  • “污泥适中,油脂适中,洗涤时间适中”；
  • “污泥越少，油脂越少，洗涤时间越短”。

    模糊控制规则如表1所示：

    xyz
    SDNGVS
    SDMGM
    SDLGL
    MDNGS
    MDMGM
    MDLGL
    LDNGM
    LDMGL
    LDLGVL

    其中：

    • SD(污泥少)、MD(污泥中)、LD(污泥多)、NG(油脂少)、MG(油脂中)、LG(油脂多)、VS(洗涤时间很短)、S(洗涤时间短)、M(洗涤时间中等)、L(洗涤时间长)、VL(洗涤时间很长)。

      1. 污泥隶属函数

      # 污泥隶属函数
sludge['SD'] = fuzz.trimf(sludge.universe, [0, 0, 50])
sludge['MD'] = fuzz.trimf(sludge.universe, [0, 50, 100])
sludge['LD'] = fuzz.trimf(sludge.universe, [50, 100, 100])

      【人工智能】实验二: 洗衣机模糊推理系统实验与基础知识

      【人工智能】实验二: 洗衣机模糊推理系统实验与基础知识

      2. 油脂隶属函数

      # 油脂隶属函数
grease['NG'] = fuzz.trimf(grease.universe, [0, 0, 50])
grease['MG'] = fuzz.trimf(grease.universe, [0, 50, 100])
grease['LG'] = fuzz.trimf(grease.universe, [50, 100, 100])

      【人工智能】实验二: 洗衣机模糊推理系统实验与基础知识

      【人工智能】实验二: 洗衣机模糊推理系统实验与基础知识

      3. 洗涤时间隶属度函数

      # 洗涤时间隶属度函数
washing_time['VS'] = fuzz.trimf(washing_time.universe, [0, 0, 30])
washing_time['S'] = fuzz.trimf(washing_time.universe, [0, 30, 60])
washing_time['M'] = fuzz.trimf(washing_time.universe, [30, 60, 90])
washing_time['L'] = fuzz.trimf(washing_time.universe, [60, 90, 120])
washing_time['VL'] = fuzz.trimf(washing_time.universe, [90, 120, 120])

      【人工智能】实验二: 洗衣机模糊推理系统实验与基础知识

      【人工智能】实验二: 洗衣机模糊推理系统实验与基础知识

      import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl
import matplotlib.pyplot as plt
# 创建输入变量和输出变量
sludge = ctrl.Antecedent(np.arange(0, 101, 1), 'sludge')
grease = ctrl.Antecedent(np.arange(0, 101, 1), 'grease')
washing_time = ctrl.Consequent(np.arange(0, 121, 1), 'washing_time')
# 定义隶属函数
sludge['SD'] = fuzz.trimf(sludge.universe, [0, 0, 50])
sludge['MD'] = fuzz.trimf(sludge.universe, [0, 50, 100])
sludge['LD'] = fuzz.trimf(sludge.universe, [50, 100, 100])
grease['NG'] = fuzz.trimf(grease.universe, [0, 0, 50])
grease['MG'] = fuzz.trimf(grease.universe, [0, 50, 100])
grease['LG'] = fuzz.trimf(grease.universe, [50, 100, 100])
washing_time['VS'] = fuzz.trimf(washing_time.universe, [0, 0, 30])
washing_time['S'] = fuzz.trimf(washing_time.universe, [0, 30, 60])
washing_time['M'] = fuzz.trimf(washing_time.universe, [30, 60, 90])
washing_time['L'] = fuzz.trimf(washing_time.universe, [60, 90, 120])
washing_time['VL'] = fuzz.trimf(washing_time.universe, [90, 120, 120])
# 定义模糊控制规则
rule1 = ctrl.Rule(sludge['SD'] & grease['NG'], washing_time['VS'])
rule2 = ctrl.Rule(sludge['SD'] & grease['MG'], washing_time['M'])
rule3 = ctrl.Rule(sludge['SD'] & grease['LG'], washing_time['L'])
rule4 = ctrl.Rule(sludge['MD'] & grease['NG'], washing_time['S'])
rule5 = ctrl.Rule(sludge['MD'] & grease['MG'], washing_time['M'])
rule6 = ctrl.Rule(sludge['MD'] & grease['LG'], washing_time['L'])
rule7 = ctrl.Rule(sludge['LD'] & grease['MG'], washing_time['M'])
rule8 = ctrl.Rule(sludge['LD'] & grease['LG'], washing_time['L'])
rule9 = ctrl.Rule(sludge['LD'] & grease['LG'], washing_time['VL'])
# 创建控制系统
washing_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8, rule9])
washing = ctrl.ControlSystemSimulation(washing_ctrl)
# 输入污泥和油脂值
washing.input['sludge'] = 70
washing.input['grease'] = 60
# 进行模糊推理
washing.compute()
# 获取洗涤时间的输出
washing_time.view(sim=washing)
# 打印输出洗涤时间
print("洗涤时间:", washing.output['washing_time'])
# 绘制隶属度函数
sludge.view()
grease.view()
washing_time.view()
plt.show()

      4. 模糊控制规则表

      【人工智能】实验二: 洗衣机模糊推理系统实验与基础知识

      注：SD（污泥少）、MD（污泥中）、LD（污泥多）、NG（油脂少）、MG（油脂中）、LG（油脂多）、VS（洗涤时间很短）、S（洗涤时间短）、M（洗涤时间中等）、L（洗涤时间长）、VL（洗涤时间很长）。

      推论结果立体图

      【人工智能】实验二: 洗衣机模糊推理系统实验与基础知识

      import numpy as np
import skfuzzy as fuzz
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from skfuzzy import control as ctrl
#定义输入输出的取值范围
#  污泥和油脂范围为[0，100]
#  洗涤时间范围为[0，120]
x_stain = np.arange(0, 101, 1)
x_oil = np.arange(0, 101, 1)
x_time = np.arange(0, 121, 1)
# 定义模糊控制变量
stain = ctrl.Antecedent(x_stain, 'stain')
oil = ctrl.Antecedent(x_oil, 'oil')
time = ctrl.Consequent(x_time, 'time')
# 生成模糊隶属函数
#函数中的三元变量，第一个代表折线的起点，第二是最大值，第三是终点
stain['SD'] = fuzz.trimf(x_stain, [0, 0, 50])  #定义污渍的三角隶属度函数横坐标
stain['MD'] = fuzz.trimf(x_stain, [0, 50, 100])
stain['LD'] = fuzz.trimf(x_stain, [50, 100, 100])
oil['NG'] = fuzz.trimf(x_oil, [0, 0, 50]) #定义油污的三角隶属度函数横坐标
oil['MG'] = fuzz.trimf(x_oil, [0, 50, 100])
oil['LG'] = fuzz.trimf(x_oil, [50, 100, 100])
time['VS'] = fuzz.trimf(x_time, [0, 0, 20]) #定义洗涤时间的三角隶属度函数横坐标
time['S'] = fuzz.trimf(x_time, [0, 20, 50])
time['M'] = fuzz.trimf(x_time, [20, 50, 80])
time['L'] = fuzz.trimf(x_time, [50, 80, 120])
time['VL'] = fuzz.trimf(x_time, [80, 120, 120])
#采用解模糊方法——质心解模糊方式
time.defuzzify_method='centroid'
#规则
rule1=ctrl.Rule(antecedent=((stain['SD'] & oil['NG'])),consequent=time['VS'],label='time=VS')
rule2=ctrl.Rule(antecedent=((stain['SD'] & oil['MG'])|(stain['MD'] & oil['MG'])|(stain['LD'] & oil['NG'])),consequent=time['M'],label='time=M')
rule3=ctrl.Rule(antecedent=((stain['SD'] & oil['LG'])|(stain['MD'] & oil['LG'])|(stain['LD'] & oil['MG'])),consequent=time['L'],label='time=L')
rule4=ctrl.Rule(antecedent=((stain['MD'] & oil['NG'])),consequent=time['S'],label='time=S')
rule5=ctrl.Rule(antecedent=((stain['LD'] & oil['LG'])),consequent=time['VL'],label='time=VL')
# 系统和运行环境初始化
rule=[rule1, rule2, rule3,rule4,rule5]
time_ctrl = ctrl.ControlSystem(rule)
wash_time = ctrl.ControlSystemSimulation(time_ctrl)
#规则中带一些奇怪的规则，处理后输出
for i in range(len(rule)):
    print("rule",i,end=":")
    for item in str(rule[i]):
        if(item!='\n'):
            print(item,end="")
        else:
            break
    print('\t')
#画图
stain.view()
oil.view()
time.view()
#time.view()
plt.show()
#绘制3D图
upsampled=np.linspace(0,101,21)#步距参数
x,y=np.meshgrid(upsampled,upsampled)
z=np.zeros_like(x)
pp=[]
for i in range(0,21):
    for j in range(0,21):
        wash_time.input['stain']=x[i,j]
        wash_time.input['oil']=y[i,j]
        wash_time.compute()
        z[i,j]=wash_time.output['time']
        pp.append(z[i,j])
print('max：',max(pp))
print('min：',min(pp))
from mpl_toolkits.mplot3d import Axes3D
fig=plt.figure(figsize=(8,8))#画布大小
ax=fig.add_subplot(111,projection='3d')
surf=ax.plot_surface(x,y,z,rstride=1,cstride=1,cmap='viridis',linewidth=0.1,antialiased=True)
ax.view_init(30,250)#观察角度
plt.title('3D results')
ax.set_xlabel('stain')
ax.set_ylabel('oil')
ax.set_zlabel('time')
plt.show()

      (2)假定当前传感器测得的信息为x0(污泥)=60，y0(油脂)=70，采用模糊决策，给出模糊推理结果，并观察模糊推理的动态仿真环境，给出其动态仿真环境图。

      import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
# 导入NumPy库用于数学运算
import numpy as np
# 在Sludge函数和Grease函数中添加输入值检查
def Sludge(a):
    sludge = [0, 0, 0]  # 默认隶属度为0，依次对应SD, MD, LD
    if a  100:
        a = 100
    if 0 'VS': '很短', 'S': '短', 'M': '中等', 'L': '长', 'VL': '很长'}
    print("模糊推理系统预测洗涤时间:{}{},预计洗涤时间:{}".format(time_sign,flag[time_sign], time_pre))

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