rbf神经网络自适应控制代码解释
时间: 2024-04-07 21:25:49 浏览: 14
RBF神经网络自适应控制是一种基于径向基函数(Radial Basis Function)的神经网络控制方法。它通过使用径向基函数来逼近未知的系统动态,并根据系统的实时反馈信息来自适应地调整网络参数,以实现对系统的控制。
以下是一个简单的RBF神经网络自适应控制的代码解释:
```python
import numpy as np
class RBFNetwork:
def __init__(self, num_inputs, num_hidden, num_outputs):
self.num_inputs = num_inputs
self.num_hidden = num_hidden
self.num_outputs = num_outputs
self.centers = np.random.uniform(-1, 1, (num_hidden, num_inputs))
self.weights = np.random.randn(num_hidden, num_outputs)
def radial_basis_function(self, x, c, s):
return np.exp(-1 / (2 * s**2) * np.linalg.norm(x - c)**2)
def forward(self, x):
hidden_layer = np.zeros(self.num_hidden)
for i in range(self.num_hidden):
hidden_layer[i] = self.radial_basis_function(x, self.centers[i], 1)
output_layer = np.dot(hidden_layer, self.weights)
return output_layer
def train(self, X, y, learning_rate=0.01, epochs=100):
for epoch in range(epochs):
for i in range(X.shape[0]):
x = X[i]
y_true = y[i]
hidden_layer = np.zeros(self.num_hidden)
for j in range(self.num_hidden):
hidden_layer[j] = self.radial_basis_function(x, self.centers[j], 1)
output_layer = np.dot(hidden_layer, self.weights)
error = y_true - output_layer
delta = learning_rate * error * hidden_layer
self.weights += delta.reshape(self.num_hidden, self.num_outputs)
# 示例用法
X = np.array([[0], [1], [2], [3], [4], [5]])
y = np.array([[-1], [0.2], [0.9], [2.1], [3.8], [5.5]])
rbf_network = RBFNetwork(num_inputs=1, num_hidden=5, num_outputs=1)
rbf_network.train(X, y)
# 使用训练好的网络进行预测
x_test = np.array([[6], [7]])
predictions = rbf_network.forward(x_test)
print(predictions)
```
上述代码实现了一个简单的RBF神经网络自适应控制。其中,`RBFNetwork`类包含了网络的初始化、前向传播和训练方法。在训练过程中,通过计算输入样本与每个径向基函数的距离来得到隐藏层的输出,然后通过矩阵乘法得到输出层的输出。根据输出与真实值之间的误差,使用梯度下降法更新网络参数。