PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

Cai, Wei; Yang, Junjie; Yu, Yidan; Song, Youyi; Zhou, Teng; Qin, Jing

doi:10.1109/access.2019.2963784

Cited by 97 publications

(58 citation statements)

References 45 publications

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“…Accurate recognition of the scenes is really relevant for applications with the purpose of context machine awareness, which is of critical importance for intelligent transportation or automatic pilot. The feature extraction method presented is general, and can be extend to other time series analysis tasks, such traffic flow forecasting [31]- [33], intelligent computing [34]- [36], or medical signal visualization [37], [38].…”

Section: E Discussionmentioning

confidence: 99%

An Audio Data Representation for Traffic Acoustic Scene Recognition

et al. 2020

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Acoustic scene recognition (ASR), recognizing acoustic environments given an audio recording of the scene, has a wide range of applications, e.g. robotic navigation and audio forensic. However, ASR remains challenging mainly due to the difficulty of representing audio data. In this paper, we focus on traffic acoustic data. Traffic acoustic sense recognition provides complementary information to visual information of the scene; for example, it can be used to verify the visual perception result. The acoustic analysis and recognition, in consideration of its simple and convenient, can effectively enhance the perception ability which only applies visual information. We propose an audio data representation method to improve the traffic acoustic scene recognition accuracy. The proposed method employs the constant Q transform (CQT) and histogram of gradient (HOG) to transfer the one-dimensional audio signals into a time-frequency representation. We also propose two data representation mechanisms, called global and local feature selections, in order to select features that are able to describe the shape of time-frequency structures. We finally exploit the least absolute shrinkage and selection operator (LASSO) technique to further improve the recognition accuracy, by further selecting the most representative information for the recognition. We implemented extensive experiments, and the results show that the proposed method is effective, significantly outperforming the state-of-the-art methods.

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Section: E Discussionmentioning

confidence: 99%

An Audio Data Representation for Traffic Acoustic Scene Recognition

et al. 2020

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“…In contrast to using original contexts, some researchers recommend choosing important contexts to improve model performance [26], whereas other researchers improve the accuracy by extracting hidden patterns from the original contexts [27]. In addition to those single models, ensemble learning [28]- [31] can be useful in CAR. For example, [29] employed gradient boosting techniques to organize and optimize rich attributes in a context-aware factorization framework.…”

Section: A New User Problemmentioning

confidence: 99%

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

et al. 2020

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Predicting new user behavior has always been a challenging issue in intelligent recommender systems. This challenge is mainly due to the extreme asymmetry of information between new users and old users. Existing factorization models can efficiently process and map asymmetric information, but they are not good at mining deep relationships between contexts when compressing high-dimensional data. In contrast, neural network methods can deeply exploit the relationship between contexts; however, their training cost is much larger than factorization approaches. Therefore, this paper proposes a scalable and efficient recommender to solve the new user problem by filling the gap between factorization models and neural networks. The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts. The efficient part is a factorization model based on a correlation constraint mechanism and a projection strategy, which enables an asymmetric mapping of information between old and new users. The entire recommender fuses the two parts so that the factorization model and the neural network can complement each other. The experimental results show that our approach can achieve a good balance between performance and training efficiency compared to state-of-the-art methods. INDEX TERMS Recommender systems, new user problems, user behavior, context-aware recommendation, efficient training.

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“…Improper network parameters setting will affect the prediction performance of the proposed method. The PSO algorithm, as one of the powerful tools of NN parameter optimization [41], is selected here to adjust DBN-ELM model structure parameters, due to its outstanding characteristics like fast convergence speed, simple structure, and good memory ability [23]. Conveniently, the PSO algorithm with time-varying weight proposed in [43] is employed, which has better search capabilities to handle the problem of premature convergence.…”

Section: Model Optimization Based On Pso Algorithmmentioning

confidence: 99%

“…Furthermore, to further improve the prediction accuracy, the particle swarm optimization (P-SO) algorithm as the optimization tool is adopted into the design of the DBN-ELM model. The PSO algorithm with the advantages of fast search speed, simple structure, and good memory ability [23] is widely used to optimize the structure [38]- [40] and parameters [23], [41], [42] of neural networks (NN). Thus, this paper uses the PSO algorithm with timevarying inertia weight [43] to adjust the structural parameters of the DBN-ELM and improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Belief Network and Extreme Learning Machine Based Performance Degradation Prediction Method for Proton Exchange Membrane Fuel Cell

Xie

Zou

et al. 2020

IEEE Access

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Lifetime and reliability seriously affect the applications of proton exchange membrane fuel cell (PEMFC). Performance degradation prediction of PEMFC is the basis for improving the lifetime and reliability of PEMFC. To overcome the lower prediction accuracy caused by uncertainty and nonlinearity characteristics of degradation voltage data, this paper proposes a novel deep belief network (DBN) and extreme learning machine (ELM) based performance degradation prediction method for PEMFC. A DBN based fuel cell degradation features extraction model is designed to extract high-quality degradation features in the original degradation data by layer-wise learning. To tackle the issues of overfitting and instability in fuel cell performance degradation prediction, an ELM with good generalization performance is introduced as a nonlinear prediction model, which can get some enhancement of prediction precision and reliability. Based on the designed DBN-ELM model, the particle swarm optimization (PSO) algorithm is used in the model training process to optimize the basic network structure of DBN-ELM further to improve the prediction accuracy of the hybrid neural network. Finally, the proposed prediction method is experimentally validated by using actual data collected from the 5-cells PEMFC stack. The results demonstrate that the proposed approach always has better prediction performance compared with the existing conventional methods, whether in the cases of various training phase or the cases of multi-step-ahead prediction. INDEX TERMS Proton exchange membrane fuel cell, degradation prediction, deep belief network, extreme learning machine, particle swarm optimization.

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PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

Cited by 97 publications

References 45 publications

An Audio Data Representation for Traffic Acoustic Scene Recognition

An Audio Data Representation for Traffic Acoustic Scene Recognition

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

A Novel Deep Belief Network and Extreme Learning Machine Based Performance Degradation Prediction Method for Proton Exchange Membrane Fuel Cell

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