Parallel computing method of deep belief networks and its application to traffic flow prediction

Zhao, Lu; Zhou, Yonghua; Lu, Huapu; Fujita, Hamido

doi:10.1016/j.knosys.2018.10.025

Cited by 97 publications

(20 citation statements)

References 25 publications

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“…Recently, parallel computing has been applied in the field of deep learning. Zhao et al [35] proposed a parallel computing method of deep belief networks for traffic flow prediction. The data features were learnt by multiple computing nodes by a master-slave parallel computing structure.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing

Guo

et al. 2020

IEEE Access

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Bearing fault diagnosis is of great significance to ensure the safe operation of mechanical equipment. This paper proposes an intelligent fault diagnosis method of rolling bearings based on deep belief network (DBN) with hyperparameter optimization by using parallel computing. Different with traditional diagnosis methods that extract the features manually depending on much prior knowledge about signal processing techniques and diagnostic expertise, DBN extracts fault features automatically by machine learning mechanism. Considering the time consuming problem, parallel computing is adopted to the DBN training process by using a Master/Slave mode to improve the training speed so that the global optimization with Genetic Algorithm and higher diagnosis accuracy can be achieved. Finally, the proposed method is verified with the public datasets provided by Case Western Reserve University (CWRU) with various fault depths in different locations and loads of rolling bearings. The results indicate that the proposed method can identify bearing faults under different conditions correctly which significantly enhances the intelligence of fault classification and reduces the time for parameter selection of deep learning models. INDEX TERMS Deep belief network, hyperparameter optimization, parallel computing, fault diagnosis I. INTRODUCTION

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing

Guo

et al. 2020

IEEE Access

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“…It should be pointed out that the local minimum poses a huge threat to the learning ability, which causes not only low efficiency but also low accuracy for nonlinear system modeling. Zhao et al proposed a master-slave parallel computing method for the DBN learning process, to reduce the time consumption of pre-training and fine-tuning [17]. Ahn et al proposed a virtual shared memory framework for the DNN learning process, called Soft Memory Box (SMB), which can share the memory of remote node among distributed processes in the nodes, thereby improving communication efficiency by parameter sharing [18].…”

Section: Proposed a Novel Learning Algorithm For Dynamic Fuzzy Neuralmentioning

confidence: 99%

An Improved Parallel MDBN With AVMD for Nonlinear System Modeling

Jin

Zhang

et al. 2020

IEEE Access

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Nonlinear system modeling using Deep Belief Network (DBN) is currently a research hotspot. However, the training process of DBN needs large amount of data to guarantee accuracy, and the traditional DBN may not meet the requirement of high-precision modeling. In this paper, we first improve the original DBN and Variational Mode Decomposition (VMD) algorithms, and on this basis, we then proposed a parallel Momentum Deep Belief Networks (MDBN) with Adaptive Variational Mode Decomposition (AVMD). Parallel AVMD-MDBN is an improved modeling method based on the deep learning model DBN. Firstly, a single raw dataset is decomposed into a specific number of sub-datasets using AVMD. Then these subdatasets are distributed among a number of improved MDBNs. A single raw dataset learning model and algorithm is extended to multiple feature extraction nodes to learn the characteristics of multiple subdatasets in parallel. Finally, the results of the multiple nodes are transmitted to the main feature extraction node to complete the regression calculation. In order to verify the effectiveness of the model, the proposed parallel AVMD-MDBN model is tested on a nonlinear dynamic system modeling, a Mackey-Glass timeseries prediction and a financial stock prediction. Our experimental results show that the proposed parallel AVMD-MDBN has better performances in terms of improving feature learning ability than that of other methods. INDEX TERMS Parallel momentum deep belief networks, adaptive variational mode decomposition, contrast divergence algorithm, nonlinear system modeling, financial stock prediction.

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“…Furthermore, CNN has a special weight sharing, deeper network can be formulated for better performance in the application of better complex visual task. Weight sharing decreases the quantity of adjustable parameters, thus increasing the training speed and reducing the threat of overfitting [10]. The formula is a feed forward neural network which is consist of three different layers: input layer, hidden layer and the output layer.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…The neuron is a computational sum of inputs and off-set of 1. The output will be [10] which the multiplication sum operation between weight and output. After the equation, the bias is added to calculate for the non-linear transformation.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Hybrid Convolutional Neural Network-Gradient Boosted Classifier for Vehicle Classification

Hernandez¹,

Fajardo²,

Medina³

2019

IJRTE

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Vehicle tracking and classification are used for intelligent transport system to provide data in terms of traffic management, routing, vehicle volume and others. A new approach will be introduced in this paper, a hybrid classifier that would detect vehicles that would be adaptable to Philippine settings. A combination of convolutional neural network and gradient boosted classifier would boost the classifying accuracy. In the discussion, CNN has outperformed other classifier in terms of accuracy while GBC got the highest AUROC and highest accuracy in terms of classifying. Although CNN and GBC is prone to overfitting, the dataset that will be used contains 1 hour of video.

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Parallel computing method of deep belief networks and its application to traffic flow prediction

Cited by 97 publications

References 25 publications

A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing

A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing

An Improved Parallel MDBN With AVMD for Nonlinear System Modeling

A Hybrid Convolutional Neural Network-Gradient Boosted Classifier for Vehicle Classification

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