首先要下載好的庫：pytorch, matplotlib
推薦使用pycharm + Anaconda的環境配置

一、生成數據集

data2.py

import numpy as np # 特征周期Tg a = [] for i in range(10):c = i + 1if c <= 6:a.append(round(0.2 + (c - 1) * 0.05, 2))elif c <= 9:a.append(round(0.55 + (c - 7) * 0.1, 2))else:a.append(round(0.9, 2))# 阻尼比theta c = [] for i in range(6):if i == 0:theta = 0.01elif i == 1:theta = 0.02elif i == 2:theta = 0.05elif i == 3:theta = 0.1elif i == 4:theta = 0.2elif i == 5:theta = 0.3c.append(theta)# 輸入矩陣，[Tg, theta, T, alpha] input_data = [] for i in range(10):for k in range(6):for j in range(12):for l in range(600):input_data.append([a[i], c[k], round(0.01 * l, 2)])if j == 0:alpha = 0.04elif j == 1:alpha = 0.08elif j == 2:alpha = 0.12elif j == 3:alpha = 0.16elif j == 4:alpha = 0.24elif j == 5:alpha = 0.28elif j == 6:alpha = 0.32elif j == 7:alpha = 0.5elif j == 8:alpha = 0.72elif j == 9:alpha = 0.9elif j == 10:alpha = 1.2elif j == 11:alpha = 1.4input_data[-1].append(alpha)target_data = []for i in range(10):for k in range(6):for j in range(12):for l in range(600):tem = input_data[i * 6 * 12 * 600 + k * 12 * 600 + j * 600 + l]Tg1 = tem[0]theta1 = tem[1]T1 = tem[2]alpha1 = tem[3]gamma = round(0.9 + (0.05 - theta1) / (0.3 + 6 * theta1), 2)eta1 = round(0.02 + (0.05 - theta1) / (4 + 32 * theta1), 3)eta2 = round(1 + (0.05 - theta1) / (0.06 + 1.6 * theta1), 2)if T1 <= 0.1:target_data.append([round(10 * (eta2 - 0.45) * alpha1 * T1 + 0.45 * alpha1, 5)])elif T1 <= Tg1:target_data.append([round(eta2 * alpha1, 5)])elif T1 <= 5 * Tg1:target_data.append([round(pow((Tg1 / T1), gamma) * eta2 * alpha1, 5)])else:target_data.append([round((eta2 * pow(0.2, gamma) - eta1 * (T1 - 5 * Tg1)) * alpha1, 5)])input_data = np.array(input_data) target_data = np.array(target_data)# print(input_data) # print(len(input_data)) # # print(target_data) # print(len(target_data))

二、搭建神經網絡

main.py

import torch from torch import nn from torch.utils.data import DataLoader import torch.utils.data as Data import data2# 數據集 input = torch.from_numpy(data2.input_data) target = torch.from_numpy(data2.target_data)input = torch.tensor(input, dtype=torch.float32) target = torch.tensor(target, dtype=torch.float32)# print(input) # print(target)# 搭建神經網絡 model = torch.nn.Sequential(torch.nn.Linear(1*4, 500),torch.nn.ReLU(),torch.nn.Linear(500, 500),torch.nn.ReLU(),torch.nn.Linear(500, 500),torch.nn.ReLU(),torch.nn.Linear(500, 1))# 損失函數和優化器 loss_fuc = nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.01)# 數據集和下載器 train_dataset = Data.TensorDataset(input, target) train_loader = DataLoader(train_dataset, batch_size=1000, shuffle=True)num_epochs = 1000000for epoch in range(num_epochs):for step, (data, target) in enumerate(train_loader):# print(data)# print(target)# breakoutput = model(data)loss = loss_fuc(output, target)# 反向傳播optimizer.zero_grad()loss.backward()optimizer.step()print('Epoch:', epoch + 1, ' ', 'Loss: ', loss.item())if loss.item() < 0.0001:breaktorch.save(model, 'net.pkl') # 保存整個網絡 torch.save(model.state_dict(), 'net_params.pkl') # 只保存網絡中的參數 (速度快, 占內存少)

三、測試神經網絡

test.py

import torch import matplotlib.pyplot as pltnet2 = torch.load('net.pkl')# 輸入參數，[Tg, theta, T, alpha] Tg = 0.35 theta = 0.05 alpha = 0.08gamma = round(0.9 + (0.05 - theta) / (0.3 + 6 * theta), 2) eta1 = round(0.02 + (0.05 - theta) / (4 + 32 * theta), 3) eta2 = round(1 + (0.05 - theta) / (0.06 + 1.6 * theta), 2)plt.subplot()x = [] target = [] prediction = []for i in range(600):T = i * 0.01x.append(round(i * 0.01, 2))if T <= 0.1:target.append([round(10 * (eta2 - 0.45) * alpha * T + 0.45 * alpha, 5)])elif T <= Tg:target.append([round(eta2 * alpha, 5)])elif T <= 5 * Tg:target.append([round(pow((Tg / T), gamma) * eta2 * alpha, 5)])else:target.append([round((eta2 * pow(0.2, gamma) - eta1 * (T - 5 * Tg)) * alpha, 5)])input = torch.Tensor([Tg, theta, T, alpha])input = torch.tensor(input, dtype=torch.float32)output = net2(input)output = torch.tensor(output, dtype=torch.float32)output = output.numpy()prediction.append(output)plt.plot(x, target, 'r-', lw=2) plt.plot(x, prediction, 'b-', lw=2) plt.show()

結果：

總結

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