python实现模糊神经网络

要实现模糊神经网络，可以使用Python中的模糊逻辑库，例如scikit-fuzzy。下面是一个简单的示例，展示了如何使用scikit-fuzzy库实现模糊神经网络：

import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl

# 创建输入和输出变量
input_var = ctrl.Antecedent(np.arange(0, 11, 1), 'input_var')
output_var = ctrl.Consequent(np.arange(0, 11, 1), 'output_var')

# 创建模糊集和隶属函数
input_var['low'] = fuzz.trimf(input_var.universe, [0, 0, 5])
input_var['medium'] = fuzz.trimf(input_var.universe, [0, 5, 10])
input_var['high'] = fuzz.trimf(input_var.universe, [5, 10, 10])

output_var['low'] = fuzz.trimf(output_var.universe, [0, 0, 5])
output_var['medium'] = fuzz.trimf(output_var.universe, [0, 5, 10])
output_var['high'] = fuzz.trimf(output_var.universe, [5, 10, 10])

# 创建模糊规则
rule1 = ctrl.Rule(input_var['low'], output_var['low'])
rule2 = ctrl.Rule(input_var['medium'], output_var['medium'])
rule3 = ctrl.Rule(input_var['high'], output_var['high'])

# 创建控制系统
fuzzy_sys = ctrl.ControlSystem([rule1, rule2, rule3])
fuzzy_ctrl = ctrl.ControlSystemSimulation(fuzzy_sys)

# 输入模糊值
fuzzy_ctrl.input['input_var'] = 6

# 运行控制系统
fuzzy_ctrl.compute()

# 输出模糊值
output = fuzzy_ctrl.output['output_var']

print("模糊神经网络的输出:", output)