# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement
#### 2.1.4 DNN in Network Measurement  
A DNN, also known as a multilayer perceptron, can be thought of as a neural network with at least one hidden layer. A feedforward neural network, or DNN, is a branch of the family of traditional artificial neural networks. Three layers make up a DNN: the input layer, the hidden layer, and the output layer. The input data are pre-processed in the layers before entering the network. In the neural network dataset, the number of input neurons is equal to the number of input features [43]. According to the general approximation theorem, a DNN relies on a large number of datasets and produces accurate and useful conclusions. In other words, by enhancing the neural network's depth and the nonlinear effect that the activation function produces, any function can be roughly approximated. A deep neural network can be refined into scenes with uniform properties and no special reliance. It applies to any deep-learning scene.  
In order to anticipate the time series of IOT traffic, [PERSON] et al. [9] proposed a method based on a DNN deep neural network, and the relationship between various communication parameters and the delay was modeled. The training data's size, depth, width, and epoch were all thoroughly examined. The prediction accuracy was then assessed using the MSE, SSE, MAE cost functions, and MAPE, with an accuracy rate of more than 98%. While modeling of DNN can more accurately and effectively depict actual complicated nonlinear issues, there are still many shortcomings in the frequency of DNN calls, data selection in the time domain, and other aspects. Layer-by-layer data pre-training, which solves the drawbacks of time-consuming and difficult manual feature designs, can be used to get the key features of each layer. Although the gradient-vanishing issue gets worse with network depth, it becomes easier for the optimization function to settle on the local optimal solution.  
To determine the download and upload bandwidth measurements of the end user's Internet connection, [PERSON] et al. [44] employed a straightforward feedforward neural network. An artificial neural network that has been trained was used to calculate the dynamic test's duration. The outcomes demonstrate that the dynamic duration strategy little affected the test outcomes. However, the dynamic duration test still saved a substantial quantity of data. The determined bandwidth's deviance is disregarded. The feedforward neural network has a straightforward structure, accurately realizes any limited training sample set, and can estimate any continuous function and square-integrable function. Ittypically outperforms feedback networks in terms of classification and pattern recognition. The feedforward neural network has many network parameters because each layer must record the overall properties of the preceding layer. The training pace will be slow or even fail to converge when the input dimension is high.  
For the time delay prediction of IOT, [PERSON] et al. [27] suggested a single-step and multi-step prediction method based on a time-series NARX recurrent neural network. The RNN model was trained by adding historical data from the time series to the input layer, and until the algorithm converged, the network weight was modified by the technological error between the network prediction outputs. Three neural network training algorithms were used; MSE was chosen as the performance function to assess prediction accuracy, and RMSE and MAPE were used as the prediction accuracy indices. The Trainlm algorithm has the best prediction accuracy, and since they are neural networks with memory functions, recursive neural networks are appropriate for continuous supervised learning issues using datasets. RNN has several drawbacks as well. Recurrent neural networks must spend a lot of time intentionally labeling due to their tree structure, and the separation of each node may induce error propagation.