41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
import numpy as np
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# 定义模糊集合的隶属度参数
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sludge = [0, 0.5, 1] # 污泥浓度模糊集
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grease = [0, 0.5, 1] # 油脂浓度模糊集
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time = [0, 0.25, 0.5, 0.75, 1] # 洗涤时间模糊集
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# 构建污泥和油脂的模糊关系矩阵（取最大值）
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sludgeandgrease = np.zeros((len(sludge), len(grease)))
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for i in range(len(sludge)):
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for j in range(len(grease)):
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sludgeandgrease[i, j] = max(sludge[i], grease[j])
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sludgeandgrease = sludgeandgrease.reshape(9, 1) # 展平为列向量
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# 构建模糊关系矩阵 R（取最小值）
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R = np.zeros((len(sludgeandgrease), len(time)))
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for i in range(len(sludgeandgrease)):
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for j in range(len(time)):
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R[i, j] = min(sludgeandgrease[i], time[j])
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# 输入模糊集的隶属度
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x1 = [0, 0.83, 0.6] # 污泥浓度实际测量隶属度
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y1 = [0, 0.71, 0.7] # 油脂浓度实际测量隶属度
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# 计算输入条件的模糊关系（取最大值）
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x1y1 = np.zeros((len(x1), len(y1)))
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for i in range(len(x1)):
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for j in range(len(y1)):
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x1y1[i, j] = max(x1[i], y1[j])
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x1y12 = x1y1.reshape(9) # 展平为一维数组
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# 合成模糊推理结果
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result = np.zeros(5)
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a = np.zeros(9)
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for i in range(5):
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for j in range(9):
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a[j] = x1y12[j] * R[j, i] # 模糊推理合成运算
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result[i] = max(a) # 取最大值作为最终结果
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print(result)
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