When training a data set, we are constantly calculating the cost function, which is the difference between predicted output and the actual output from a set of labelled training data.The cost function is then minimized by adjusting the weights and biases values until the lowest value is obtained. The weights from the trained DBN can be used as the initialized weights of a DNN [8, 30], and, then, all of the weights are fine-tuned by applying backpropagation or other discriminative algorithms to improve the performance of the whole network. Then the top layer RBM learns the distribution of p(v, label, h). Deep learning consists of deep networks of varying topologies. We are committed to sharing findings related to COVID-19 as quickly as possible. The accuracy of correct prediction has become so accurate that recently at a Google Pattern Recognition Challenge, a deep net beat a human. RNNSare neural networks in which data can flow in any direction. In the study, the concentrations of , NO2, and SO2 were predicted 12 hours in advance, so, horizon was set to 12. The experimental results of hourly concentration forecasting for a 12h horizon are shown in Table 3, where the best results are marked with italic. Training the data sets forms an important part of Deep Learning models. The hourly concentrations of , NO2, and SO2 at the station were predicted 12 hours in advance. The vectors are useful in dimensionality reduction; the vector compresses the raw data into smaller number of essential dimensions. As soon as you start training, the weights are changed in … The MTL-DBN-DNN model can fulfill prediction tasks at the same time by using shared information. • DBN was exploited to select the initial parameters of deep neural network (DNN The Setting of the Structures and Parameters. In order to get a better prediction of future concentrations, the sliding window [26, 27] is used to take the recent data to dynamically adjust the parameters of prediction model. DBN is a probabilistic generative model composed of multiple simple learning modules (Hinton et al., 2006; Tamilselvan and Wang, 2013). Multitask learning exploits commonalities among different learning tasks. Three transport corridors, namely, southeast branch (a), northwest branch (b), and southwest branch (c), tracked by 24 h backward trajectories of air masses in Jing-Jin-Ji area. Multitask learning can improve learning for one task by using the information contained in the training data of other related tasks [16]. The R Language. Section 2 presents the background knowledge of multitask learning, deep belief networks, and DBN-DNN and describes DBN-DNN model with multitask learning (MTL-DBN-DNN). Three transport corridors are tracked by 24 h backward trajectories of air masses in Jing-Jin-Ji area [3, 35], and they are presented in Figure 4. DBN is used to learn feature representations, and several related tasks are solved simultaneously by using shared representations. We need a very small set of labelled samples so that the features and patterns can be associated with a name. Artificial neural networks can be used as a nonlinear system to express complex nonlinear maps, so they have been frequently applied to real-time air quality forecasting (e.g., [1–5]). Jiangeng Li, Xingyang Shao, Rihui Sun, "A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction", Journal of Control Science and Engineering, vol. (4) Air-Quality-Prediction-Hackathon-Winning-Model (Winning-Model) [36]. RBM is the mathematical equivalent of a two-way translator. Long short-term memory networks (LSTMs) are most commonly used RNNs. Multitask learning can improve learning for one task by using the information contained in the training data of other related tasks. In the model, DBN is used to learn feature representations. We have a new model that finally solves the problem of vanishing gradient. In a GAN, one neural network, known as the generator, generates new data instances, while the other, the discriminator, evaluates them for authenticity. Such connection effectively avoids the problem that fully connected networks need to juggle the learning of each task while being trained, so that the trained networks cannot get optimal prediction accuracy for each task. A backward pass meanwhile takes this set of numbers and translates them back into reconstructed inputs. Therefore, for complex patterns like a human face, shallow neural networks fail and have no alternative but to go for deep neural networks with more layers. Credit assignment path (CAP) in a neural network is the series of transformations starting from the input to the output. LSTM derives from neural network architectures and is based on the concept of a memory cell. (2) DBN-DNN model using online forecasting method (OL-DBN-DNN). The day of year (DAY) [3] was used as a representation of the different times of a year, and it is calculated by where represents the ordinal number of the day in the year and T is the number of days in this year. The sliding window is used to take the recent data to dynamically adjust the parameters of the MTL-DBN-DNN model. Weather has 17 different conditions, and they are sunny, cloudy, overcast, rainy, sprinkle, moderate rain, heaver rain, rain storm, thunder storm, freezing rain, snowy, light snow, moderate snow, heavy snow, foggy, sand storm, and dusty. The performance of OL-MTL-DBN-DNN surpasses the performance of OL-DBN-DNN, which shows that multitask learning is an effective approach to improve the forecasting accuracy of air pollutant concentration and demonstrates that it is necessary to share the information contained in the training data of three prediction tasks. DL nets are increasingly used for dynamic images apart from static ones and for time series and text analysis. The firing or activation of a neural net classifier produces a score. Deep belief network is used to extract better feature representations, and several related tasks are solved simultaneously by using shared representations. In either steps, the weights and the biases have a critical role; they help the RBM in decoding the interrelationships between the inputs and in deciding which inputs are essential in detecting patterns. Studies have showed that sulfate () is a major PM constituent in the atmosphere [23]. It also includes a classifier based on the BDN, i.e., the visible units of the top layer include not only the input but also the labels. For object recognition, we use a RNTN or a convolutional network. How to choose a deep net? 딥 빌리프 네트워크(Deep Belief Network : DBN) 개념 RBM을 이용해서 MLP(Multilayer Perceptron)의 Weight를 input 데이터들만을 보고(unsuperivesd로) Pretraining 시켜서 학습이 잘 일어날 수 있는 초기 세팅.. In order to verify whether the application of multitask learning and online forecasting can improve the DBN-DNN forecasting accuracy, respectively, and assess the capability of the proposed MTL-DBN-DNN to predict air pollutant concentration, we compared the proposed MTL-DBN-DNN model with four baseline models (2-5): (1) DBN-DNN model with multitask learning using online forecasting method (OL-MTL-DBN-DNN). The sigmoid function is used as the activation function of the output layer. Neural networks are widely used in supervised learning and reinforcement learning problems. Since the dataset used in this study was released by the authors of [34], the experimental results given in the original paper for the FFA model were quoted for comparison. Copyright © 2019 Jiangeng Li et al. The main purpose of a neural network is to receive a set of inputs, perform progressively complex calculations on them, and give output to solve real world problems like classification. Collobert and Weston demonstrated that a unified neural network architecture, trained jointly on related tasks, provides more accurate prediction results than a network trained only on a single task [22]. The MTL-DBN-DNN model can fulfill prediction tasks at the same time by using shared information. Sun, T. Li, Q. Li, Y. Huang, and Y. Li, “Deep belief echo-state network and its application to time series prediction,”, T. Kuremoto, S. Kimura, K. Kobayashi, and M. Obayashi, “Time series forecasting using a deep belief network with restricted Boltzmann machines,”, F. Shen, J. Chao, and J. 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Collobert and J. Weston, “A unified architecture for natural language processing: deep neural networks with multitask learning,” in, R. M. Harrison, A. M. Jones, and R. G. Lawrence, “Major component composition of PM10 and PM2.5 from roadside and urban background sites,”, G. Wang, R. Zhang, M. E. Gomez et al., “Persistent sulfate formation from London Fog to Chinese haze,”, Y. Cheng, G. Zheng, C. Wei et al., “Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China,”, D. Agrawal and A. E. Abbadi, “Supporting sliding window queries for continuous data streams,” in, K. B. Shaban, A. Kadri, and E. Rezk, “Urban air pollution monitoring system with forecasting models,”, L. Deng and D. Yu, “Deep learning: methods and applications,” in. Training a Deep neural network with weights initialized by DBN. The four models were used to predict the concentrations of three kinds of pollutants in the same period. Then we used the monitoring data of the concentrations of six kinds of air pollutants from a station located in the city to represent the current pollutant concentrations of the selected nearby city. MTL-DBN-DNN model can solve several related prediction tasks at the same time by using shared information contained in the training data of different tasks. DL deals with training large neural networks with complex input output transformations. B. Oktay, “Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks,”. For the sake of fair comparison, we selected original 1220 elements contained in the window before sliding window begins to slide forward, and used samples corresponding to these elements as the training samples of the static prediction models (DBN-DNN and Winning-Model). Each unit in the output layer is connected to only a subset of units in the last hidden layer of DBN. However, the number of weights and biases will exponentially increase. Comparison with multiple baseline models shows that the proposed MTL-DBN-DNN achieve state-of-art performance on air pollutant concentration forecasting. Each data element together with the features that determine the element constitute a training sample , where , , and represent concentration, NO2 concentration and SO2 concentration, respectively. The MTL-DBN-DNN model is evaluated with a dataset from Microsoft Research. Neural nets have been around for more than 50 years; but only now they have risen into prominence. Deep networks have significantly greater representational power than shallow networks [6]. In this section, a DBN-based multitask deep neural network prediction model is proposed to solve multiple related tasks simultaneously by using shared information contained in the training data of different tasks. However, there are correlations between some air pollutants predicted by us so that there is a certain relevance between different prediction tasks. Several related problems are solved at the same time by using a shared representation. This work was supported by National Natural Science Foundation of China (61873008) and Beijing Municipal Natural Science Foundation (4182008). Firstly, the DBN Neural Network is used to carry out auto correlation analysis of the original data, and the characteristics of the data inclusion are obtained. To protect human health and the environment, accurate real-time air quality prediction is sorely needed. In this study, we used a data set that was collected in (Urban Computing Team, Microsoft Research) Urban Air project over a period of one year (from 1 May 2014 to 30 April 2015) [34]. The idea behind convolutional neural networks is the idea of a “moving filter” which passes through the image. When the MTL-DBN-DNN model is used for time series forecasting, the parameters of model can be dynamically adjusted according to the recent monitoring data taken by the sliding window to achieve online forecasting. These images are much larger(400×400) than 30×30 images which most of the neural nets algorithms have been tested (mnist ,stl). , SO2, and NO2 have chemical reaction and almost the same concentration trend, so we apply the proposed model to the case study on the concentration forecasting of three kinds of air pollutants 12 hours in advance. For each task, we used random forest to test the feature subsets from top1-topn according to the feature importance ranking, and then selected the first n features corresponding to the minimum value of the MAE as the optimal feature subset. The MTL-DBN-DNN model is learned with unsupervised DBN pretraining followed by backpropagation fine-tuning. deep-belief-network. A Deep Belief Network (DBN) is a multi-layer generative graphical model. This generated image is given as input to the discriminator network along with a stream of images taken from the actual dataset. GANs can be taught to create parallel worlds strikingly similar to our own in any domain: images, music, speech, prose. 还有其它的方法,鉴于鄙人才疏学浅,暂以偏概全。 4.1深度神经网络(Deep neural network) 深度学习的概念源于人工神经网络的研究。含多隐层的多层感知器就是一种深度学习结构。 [34]. For multitask learning, a deep neural network with local connections is used in the study. In the pretraining stage, the learning rate was set to 0.00001, and the number of training epochs was set to 50. The usual way of training a network: You want to train a neural network to perform a task (e.g. Based on the above two reasons, the last (fully connected) layer is replaced by a locally connected layer, and each unit in the output layer is connected to only a subset of units in the previous layer. For speech recognition, we use recurrent net. To solve several difficulties of training deep networks, Hinton et al. Learn the Neural Network from this Neural Network Tutorial. When DBN is used to initialize the parameters of a DNN, the resulting network is called DBN-DNN [31]. Comparison with multiple baseline models shows our model MTL-DBN-DNN has a stronger capability of predicting air pollutant concentration. A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction, College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China, Journal of Control Science and Engineering, http://deeplearning.stanford.edu/wiki/index.php/Deep_Networks:_Overview, https://github.com/benhamner/Air-Quality-Prediction-Hackathon-Winning-Model, The current CO concentration of the target station (, The current CO concentration of the selected nearby station (, P. S. G. De Mattos Neto, F. Madeiro, T. A. E. Ferreira, and G. D. C. Cavalcanti, “Hybrid intelligent system for air quality forecasting using phase adjustment,”, K. Siwek and S. Osowski, “Improving the accuracy of prediction of PM, X. Feng, Q. Li, Y. Zhu, J. Hou, L. Jin, and J. Wang, “Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation,”, W. Tamas, G. Notton, C. Paoli, M.-L. Nivet, and C. Voyant, “Hybridization of air quality forecasting models using machine learning and clustering: An original approach to detect pollutant peaks,”, A. Kurt and A. After a layer of RBM has been trained, the representations of the previous hidden layer are used as inputs for the next hidden layer. The reason is that they are hard to train; when we try to train them with a method called back propagation, we run into a problem called vanishing or exploding gradients.When that happens, training takes a longer time and accuracy takes a back-seat. The weights and biases change from layer to layer. Because the first two models above are the models that use online forecasting method, the training set changes over time. In 2006, a breakthrough was achieved in tackling the issue of vanishing gradients. If the dataset is not a computer vision one, then DBNs can most definitely perform better. The network needs not only to learn the commonalities of multiple tasks but also to learn the differences of multiple tasks. The locally connected architecture can well learn the commonalities and differences of multiple tasks. In the pictures, time is measured along the horizontal axis and the concentrations of three kinds of air pollutants (, NO2, SO2) are measured along the vertical axis. DBN是由Hinton在2006年提出的一种概率生成模型, 由多个限制玻尔兹曼机(RBM)[3]堆栈而成: 在训练时, Hinton采用了逐层无监督的方法来学习参数。 It is quite amazing how well this seems to work. 그런데 DBN은 하위 layer부터 상위 layer를 만들어 나가겠다! At the locally connected layer, each output node has a portion of hidden nodes that are only connected to it, and it is assumed that the number of nodes in this part is β, then 0 < β < 1/N. Window size was equal to 1220; that is, the sliding window always contained 1220 elements. In the model, each unit in the output layer is connected to only a subset of units in the last hidden layer of DBN. The locally connected architecture can well learn the commonalities and differences of multiple tasks. 기존의 Neural Network System. In deep learning, the number of hidden layers, mostly non-linear, can be large; say about 1000 layers. According to the practical guide for training RBMs in technical report [33] and the dataset used in the study, we set the architecture and parameters of the deep neural network as follows. 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