基于 Python 的双层神经网络模型预测厚度、孔隙率和压缩回弹性率

import numpy as np
import pandas as pd
import pickle

# 读取训练数据
data = pd.read_excel('C题.xlsx')
X = data[['接收距离（cm）', '热风速度(r/min)']].values
y = data[['厚度mm', '孔隙率（%）', '压缩回弹性率（%）']].values

# 定义激活函数（sigmoid函数）
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# 定义激活函数的导数
def sigmoid_derivative(x):
    return x * (1 - x)

# 初始化权重和偏置
np.random.seed(0)
weights1 = 2 * np.random.random((2, 4)) - 1
weights2 = 2 * np.random.random((4, 3)) - 1
bias1 = np.zeros((1, 4))
bias2 = np.zeros((1, 3))

# 定义训练迭代次数和学习率
epochs = 10000
learning_rate = 0.1

# 训练模型
for i in range(epochs):
    # 前向传播
    layer1_output = sigmoid(np.dot(X, weights1) + bias1)
    layer2_output = sigmoid(np.dot(layer1_output, weights2) + bias2)

    # 计算误差
    layer2_error = y - layer2_output
    if i % 1000 == 0:
        print('Error after', i, 'epochs:', np.mean(np.abs(layer2_error)))

    # 反向传播
    layer2_delta = layer2_error * sigmoid_derivative(layer2_output)
    layer1_error = layer2_delta.dot(weights2.T)
    layer1_delta = layer1_error * sigmoid_derivative(layer1_output)

    # 更新权重和偏置
    weights2 += layer1_output.T.dot(layer2_delta) * learning_rate
    bias2 += np.sum(layer2_delta, axis=0, keepdims=True) * learning_rate
    weights1 += X.T.dot(layer1_delta) * learning_rate
    bias1 += np.sum(layer1_delta, axis=0, keepdims=True) * learning_rate

# 测试模型
test_data = pd.read_excel('C题.xlsx')
X_test = test_data[['接收距离（cm）', '热风速度(r/min)']].values
y_true = test_data[['厚度mm', '孔隙率（%）', '压缩回弹性率（%）']].values

layer1_output_test = sigmoid(np.dot(X_test, weights1) + bias1)
layer2_output_test = sigmoid(np.dot(layer1_output_test, weights2) + bias2)

print('Predicted Output:', layer2_output_test)
print('True Output:', y_true)

# 保存模型
model = {
    'weights1': weights1,
    'weights2': weights2,
    'bias1': bias1,
    'bias2': bias2
}
with open('model.pkl', 'wb') as file:
    pickle.dump(model, file)

# 读取新数据
pre_data = pd.read_excel('预测数据.xlsx')
X_pre = pre_data[['接收距离（cm）', '热风速度(r/min)']].values
将新数据中的接收距离和热风速度带入上述模型预测出厚度mm', '孔隙率（%）', '压缩回弹性率（%）的python代码内容：import numpy as np
import pandas as pd
import pickle

# 读取模型
with open('model.pkl', 'rb') as file:
    model = pickle.load(file)
    
weights1 = model['weights1']
weights2 = model['weights2']
bias1 = model['bias1']
bias2 = model['bias2']

# 读取新数据
pre_data = pd.read_excel('预测数据.xlsx')
X_pre = pre_data[['接收距离（cm）', '热风速度(r/min)']].values

# 预测新数据
layer1_output_pre = sigmoid(np.dot(X_pre, weights1) + bias1)
layer2_output_pre = sigmoid(np.dot(layer1_output_pre, weights2) + bias2)

print('Predicted Output:', layer2_output_pre)