人工智能洗衣机和全自动有什么区别（人工智能洗衣机模糊推理系统实验（课本实验））

时间2025-06-20 07:15:02分类IT科技浏览5447

导读：一、实验目的...

一、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

二、实验内容

用python设计洗衣机洗涤时间的模糊控制

三、实验要求

已知人的操作经验是

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

洗衣机的模糊控制规则表

x y z SD NG VS SD MG M SD LG L MD NG S MD MG M MD LG L LD NG M LD MG L LD LG VL

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）假设污泥、油脂、洗涤时间的论域分别为[0 ，100] [0,100] [0,120],设计相应的模糊推理系统，给出输入、输出语言变量的隶属函数图，模糊控制规则表和推论结果立体图。

（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。

第一小题，代码如下

#需要先安装pip install scikit-fuzzy #released in 2021.2 """ ========================================== Fuzzy Control Systems: The washtimeping Problem ========================================== The washtimeping problem is commonly used to illustrate the power of fuzzy logic principles to generate complex behavior from a compact, intuitive set of expert rules. If youre new to the world of fuzzy control systems, you might want to check out the `Fuzzy Control Primer <../userguide/fuzzy_control_primer.html>`_ before reading through this worked example. The washtimeping Problem ------------------- Lets create a fuzzy control system which models how you might choose to washtime at a restaurant. When washtimeping, you consider the oil and stain, rated between 0 and 10. You use this to leave a washtime of between 0 and 25%. We would formulate this problem as: * Antecedents (Inputs) - `oil` * How was the oil on a scale of 0 to 100? * Fuzzy set (ie, fuzzy value range): poor （SD）, acceptable（MD）, amazing （LD） - `stain` * Universe: stain on a scale of 0 to 100? * Fuzzy set: bad, decent, great * Consequents (Outputs) - `washtime` * Universe: How much should we washtime, on a scale of 0 to 120 * Fuzzy set: low, medium, high * Rules - refer to P302 * Usage - If I tell this controller that I rated: * the oil as 10, and * the stain as 10, - it would recommend : * a 29 washtime. Creating the washtimeping Controller Using the skfuzzy control API ------------------------------------------------------------- We can use the `skfuzzy` control system API to model this. First, lets define fuzzy variables """ import numpy as np import skfuzzy as fuzz from skfuzzy import control as ctrl import matplotlib.pyplot as plt # New Antecedent/Consequent objects hold universe variables and membership # functions stain = ctrl.Antecedent(np.arange(0, 101, 1), stain) oil = ctrl.Antecedent(np.arange(0, 101, 1), oil) washtime = ctrl.Consequent(np.arange(0, 120, 1), washtime) # Auto-membership function population is possible with .automf(3, 5, or 7) stain.automf(3, variable_type=quant) oil.automf(3, variable_type=quant) # Custom membership functions can be built interactively with a familiar, # Pythonic API washtime[VS] = fuzz.trimf(washtime.universe, [0, 0, 20]) washtime[S] = fuzz.trimf(washtime.universe, [0, 20, 50]) washtime[M] = fuzz.trimf(washtime.universe, [20, 50, 70]) washtime[L] = fuzz.trimf(washtime.universe, [50, 70, 100]) washtime[VL] = fuzz.trimf(washtime.universe, [70, 100, 120]) """ To help understand what the membership looks like, use the ``view`` methods. These return the matplotlib `Figure` and `Axis` objects. They are persistent as written in Jupyter notebooks; other environments may require a `plt.show()` command after each `.view()`. """ # You can see how these look with .view() stain[average].view() plt.show() oil.view() plt.show() washtime.view() plt.show() """ .. image:: PLOT2RST.current_figure Fuzzy rules ----------- Now, to make these triangles useful, we define the *fuzzy relationship* between input and output variables. """ # low = SD or NG；average = MD or MG；high=LD or LG rule1 = ctrl.Rule(stain[low] & oil[low], washtime[VS]) rule2 = ctrl.Rule(stain[low] & oil[average], washtime[M]) rule3 = ctrl.Rule(stain[low] & oil[high], washtime[L]) rule4 = ctrl.Rule(stain[average] & oil[low], washtime[S]) rule5 = ctrl.Rule(stain[average] & oil[average], washtime[M]) rule6 = ctrl.Rule(stain[average] & oil[high], washtime[L]) rule7 = ctrl.Rule(stain[high] & oil[low], washtime[M]) rule8 = ctrl.Rule(stain[high] & oil[average], washtime[L]) rule9 = ctrl.Rule(stain[high] & oil[high], washtime[VL]) """ .. image:: PLOT2RST.current_figure Control System Creation and Simulation --------------------------------------- Now that we have our rules defined, we can simply create a control system via: """ washtimeping_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8, rule9]) """ In order to simulate this control system, we will create a ``ControlSystemSimulation``. Think of this object representing our controller applied to a specific set of circumstances. For washtimeping, this might be washtimeping Sharon at the local brew-pub. We would create another ``ControlSystemSimulation`` when were trying to apply our ``washtimeping_ctrl`` for Travis at the cafe because the inputs would be different. """ washtimeping = ctrl.ControlSystemSimulation(washtimeping_ctrl) """ We can now simulate our control system by simply specifying the inputs and calling the ``compute`` method. """ # Pass inputs to the ControlSystem using Antecedent labels with Pythonic API # Note: if you like passing many inputs all at once, use .inputs(dict_of_data) washtimeping.input[stain] = 2 washtimeping.input[oil] = 2 # Crunch the numbers washtimeping.compute() """ Once computed, we can view the result as well as visualize it. """ print(washtimeping.output[washtime]) washtime.view(sim=washtimeping) plt.show()

第二小题代码在第一小题基础上进行稍微改变

改为 washtimeping.input[stain] = 60 就可以了

washtimeping.input[oil] = 70

运行结果如下：

好了，就这些，大家如果觉得有帮助的话就太好了，我做实验的时候就没有找到这些。