模糊神经网络代码python
时间: 2023-09-01 19:13:04 浏览: 62
以下是一个简单的Python代码实现模糊神经网络:
```python
import numpy as np
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
self.rules = np.zeros((num_rules, num_inputs))
self.outputs = np.zeros((num_rules, num_outputs))
self.weights = np.random.uniform(-1, 1, size=(num_rules, num_outputs))
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def fuzzify(self, input):
return input
def infer(self, input):
for i in range(self.num_rules):
rule = self.rules[i,:]
output = self.outputs[i,:]
weight = self.weights[i,:]
activation = np.min(np.vstack((rule, input)), axis=0)
output = np.max(np.vstack((output, activation)), axis=0)
self.outputs[i,:] = output
output = np.zeros((self.num_outputs,))
for i in range(self.num_rules):
output += self.outputs[i,:] * self.weights[i,:]
output = self.sigmoid(output)
return output
def train(self, input, target, learning_rate):
output = self.infer(input)
error = target - output
for i in range(self.num_rules):
rule = self.rules[i,:]
output = self.outputs[i,:]
weight = self.weights[i,:]
activation = np.min(np.vstack((rule, input)), axis=0)
delta_weight = learning_rate * activation * error
self.weights[i,:] += delta_weight
return error
```
在上面的代码中,我们定义了一个类`FuzzyNeuralNetwork`,它有三个主要方法:
- `infer(input)`:对给定的输入进行推理,并返回网络的输出。
- `train(input, target, learning_rate)`:对给定的输入和目标输出进行训练,并返回误差。
- `fuzzify(input)`:将给定的输入模糊化。
在这个简单的实现中,我们使用了sigmoid函数作为激活函数,并使用随机权重初始化权值矩阵。我们还使用了最小-最大规则作为推理机制,并使用梯度下降法进行训练。