模糊神经网络python代码
时间: 2023-07-01 22:04:24 浏览: 41
以下是一个简单的模糊神经网络的 Python 代码示例:
```python
import numpy as np
# 模糊神经网络类
class FuzzyNeuralNetwork:
def __init__(self, num_inputs, num_outputs, num_rules):
self.num_inputs = num_inputs
self.num_outputs = num_outputs
self.num_rules = num_rules
# 随机初始化权重矩阵
self.weights = np.random.rand(self.num_inputs, self.num_rules, self.num_outputs)
# 模糊推理函数
def fuzzy_inference(self, inputs):
# 模糊化输入
fuzzy_inputs = np.zeros((self.num_inputs, self.num_rules))
for i in range(self.num_inputs):
for j in range(self.num_rules):
fuzzy_inputs[i][j] = self.membership_function(inputs[i], j)
# 计算规则权重
rule_weights = np.zeros(self.num_rules)
for j in range(self.num_rules):
rule_weights[j] = np.prod(fuzzy_inputs[:,j])
# 计算输出
outputs = np.zeros(self.num_outputs)
for k in range(self.num_outputs):
for j in range(self.num_rules):
outputs[k] += rule_weights[j] * self.weights[:,j,k].sum()
return outputs
# 成员函数
def membership_function(self, x, j):
# 这里使用三角形成员函数
a = j / (self.num_rules - 1)
b = 1 / (self.num_rules - 1)
if x <= a - b or x >= a + b:
return 0
elif x > a - b and x <= a:
return (x - (a - b)) / b
elif x > a and x < a + b:
return ((a + b) - x) / b
else:
return 0
```
以上代码实现了一个简单的模糊神经网络,包括模糊推理函数和成员函数。其中,`num_inputs` 为输入变量个数,`num_outputs` 为输出变量个数,`num_rules` 为规则数量。权重矩阵 `weights` 初始化为随机值。成员函数使用了三角形成员函数。