rbf神经网络自适应控制代码

Sure, here is an example of RBF (Radial Basis Function) neural network adaptive control code using Python and TensorFlow:

import numpy as np
import tensorflow as tf

# Define the RBF neural network class
class RBFNetwork:
    def __init__(self, input_dim, hidden_dim, output_dim):
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.centers = tf.Variable(tf.random.normal([hidden_dim, input_dim]))
        self.beta = tf.Variable(tf.ones([hidden_dim]))
        self.weights = tf.Variable(tf.random.normal([hidden_dim, output_dim]))

    def rbf(self, X):
        return tf.exp(-self.beta * tf.reduce_sum(tf.square(tf.subtract(X, self.centers)), axis=1))

    def predict(self, X):
        radial_func = self.rbf(X)
        return tf.matmul(radial_func, self.weights)

# Training data
X_train = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])
y_train = np.array([[1], [2], [3]])

# Model parameters
input_dim = X_train.shape[1]
hidden_dim = 10
output_dim = y_train.shape[1]

# Create an instance of the RBF network
model = RBFNetwork(input_dim, hidden_dim, output_dim)

# Define loss function and optimizer
loss_fn = tf.keras.losses.MeanSquaredError()
optimizer = tf.keras.optimizers.Adam()

# Training loop
epochs = 100
for epoch in range(epochs):
    with tf.GradientTape() as tape:
        y_pred = model.predict(X_train)
        loss_value = loss_fn(y_train, y_pred)
    gradients = tape.gradient(loss_value, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

# Test the model
X_test = np.array([[0.7, 0.8], [0.9, 1.0]])
y_pred = model.predict(X_test)
print("Predicted values:")
print(y_pred)

In this code, we define a `RBFNetwork` class that represents the RBF neural network. It has methods for computing the RBF function and making predictions. The training loop uses the mean squared error loss function and the Adam optimizer to update the model's parameters. Finally, we test the trained model by making predictions on some test data.

Please note that this is just a simple example to demonstrate the implementation of RBF neural network adaptive control. You may need to modify it based on your specific requirements and data.