Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection

Yin, Biao; Dridi, Mahjoub; Moudni, Abdellah El

doi:10.1007/s00521-017-3066-9

Cited by 11 publications

(9 citation statements)

References 39 publications

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“…The training parameters of the proposed algorithm are tuned after repeating the experiment in reason to attempt the best score value in Equation 17and the minimum RMSE using Equation 18for the test set in both Equations (17) and (18).…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…After the training model is initialized, the USS-based TD objective function is proposed to adopt the RLS weights to the new data variations [18]. Furthermore, we proposed, based on [8,18,26], a new DFF formula that is proposed as illustrated in Equation 10, so Equations (4) and (5) will be changed, respectively, into (9) and (10).…”

Section: Proposed Os-elmmentioning

confidence: 99%

“…Lu et al [17] enhanced the recursive learning of the OS-ELM by introducing ensemble Kalman filter propagation for output weights tuning of NN under RUL prediction of aircraft engines. Yin et al [18] proposed a (TD-RLS(λ)) with a forgetting factor to enhance the RLS adaptation to linear function approximation by updating its parameters based on environmental feedback.…”

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confidence: 99%

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Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

et al. 2020

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The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: Proposed Os-elmmentioning

confidence: 99%

mentioning

confidence: 99%

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Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

et al. 2020

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“…Each traffic control technique was evaluated using the SUMO simulator, and the result showed that ATOM is efficient during road conditions with high congestion. Publication written by Yin et al [41] analyses adaptive traffic light applied to a single two‐lane intersection environment. The experiment analysed which of five control schemes fits best for such a problem.…”

Section: Literature Analysismentioning

confidence: 99%

Recent advances in traffic optimisation: systematic literature review of modern models, methods and algorithms

Rydzewski¹,

Czarnul²

2020

IET intell. transp. syst

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Over the past few decades, the increasing number of vehicles and imperfect road traffic management have been sources of congestion in cities and reasons for deteriorating health of its inhabitants. With the help of computer simulations, transport engineers optimise and improve the capacity of city streets. However, with an enormous number of possible simulation types, it is difficult to grasp valuable, innovative solutions which are of the greatest value to city citizens. In this work, the authors expose various problems within this area having reviewed and analysed over 130 papers selected out of 1200 works in the field of urban simulations. The study describes the selection process of important papers and highlights characteristics of microsimulations, macrosimulations, computation optimisations and other approaches found in the literature which are especially useful and should be further built on in the future. They present and compare results provided in reviewed works in terms of throughput improvement, queue, waiting and travel time reduction, vehicle speed increase, speed‐ups as well as assumed simulation parameters. Finally, they focus on research gaps, such as a small number of works considering crisis simulations, few real‐world scale simulations as well as on software architectural changes and low‐level optimizations.

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“…Some methodologies for estimating model parameters have been proposed using the RLS method, which updates a vector of parameters and has a lower computational cost than the method of non-recursive least squares [5]. Many researches were developed looking for an improvement of the RLS using other algorithms like neural networks, fuzzy systems, network based fuzzy inference system (ANFIS) and metaheuristics [23]- [29].…”

Section: Introductionmentioning

confidence: 99%

Identification by Recursive Least Squares With Kalman Filter (RLS-KF) Applied to a Robotic Manipulator

et al. 2021

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The field of robotics has grown a lot over the years due to the increasing necessity of industrial production and the search for quality of industrialized products. The identification of a system requires that the model output be as close as possible to the real one, in order to improve the control system. Some hybrid identification methods can improve model estimation through computational intelligence techniques, mainly improving the limitations of a given linear technique. This paper presents as a main contribution a hybrid algorithm for the identification of industrial robotic manipulators based on the recursive least square (RLS) method, which has its matrix of regressors and vector of parameters optimized via the Kalman filter (KF) method (RLS-KF). It is also possible to highlight other contributions, which are the identification of a robotic joint driven by a three-phase induction motor, the comparison of the RLS-KF algorithm with RLS and extended recursive least square (ERLS) and the generation of the transfer function by each method. The results are compared with the well-known recursive least squares and extended recursive least squares considering the criteria of adjustable coefficient of determination (R 2 a ) and computational cost. The RLS-KF showed better results compared to the other two algorithms (RLS and ERLS). All methods have generated their respective transfer functions.

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Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection

Cited by 11 publications

References 39 publications

Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

Recent advances in traffic optimisation: systematic literature review of modern models, methods and algorithms

Identification by Recursive Least Squares With Kalman Filter (RLS-KF) Applied to a Robotic Manipulator

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