模糊推理python代码
时间: 2023-11-08 14:59:10 浏览: 53
模糊推理是一种基于模糊逻辑的推理方法,用于处理不确定性和模糊性问题。下面是一个简单的Python实现模糊推理的代码示例:
```python
# 导入必要的模块
import numpy as np
# 定义模糊推理函数
def fuzzy_inference(rule_base, input_values):
# 初始化输出变量
output = 0
# 循环遍历每条规则
for rule in rule_base:
# 获取规则的条件和结论
conditions = rule[:-1]
conclusion = rule[-1]
# 计算条件的匹配度
match_degree = 1
for i in range(len(conditions)):
if input_values[i] < conditions[i][0] or input_values[i] > conditions[i][2]:
match_degree = 0
break
elif input_values[i] < conditions[i][1]:
match_degree = min(match_degree, (input_values[i] - conditions[i][0]) / (conditions[i][1] - conditions[i][0]))
else:
match_degree = min(match_degree, (conditions[i][2] - input_values[i]) / (conditions[i][2] - conditions[i][1]))
# 更新输出变量
output += match_degree * conclusion
return output
# 定义模糊规则库
rule_base = [
[(0, 0.5, 1), (0, 0.5, 1), (0.3, 0.6, 1.0), 1.0],
# 其他规则...
]
# 定义输入变量
input_values = [0.5, 0.8, 0.4]
# 进行模糊推理
output = fuzzy_inference(rule_base, input_values)
# 打印输出结果
print("模糊推理的输出结果为:", output)
```