Tracking Moving Agents via Inexact Online Gradient Descent Algorithm

Bedi, Amrit Singh; Sarma, P.S.; Rajawat, Ketan

doi:10.1109/jstsp.2018.2797423

Cited by 37 publications

(26 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the dynamic regret bounds also depend on the possibly stochastic error sequence e k [21]. Here we develop bounds on the expected regret in terms of the cumulative mean error Likewise e k may diminish if the noisy gradients available from the adversary can be corrected or improved with time.…”

Section: Problem Formulationmentioning

confidence: 99%

Online Learning With Inexact Proximal Online Gradient Descent Algorithms

Dixit

Bedi

Tripathi

et al. 2019

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

We consider non-differentiable dynamic optimization problems such as those arising in robotics and subspace tracking. Given the computational constraints and the time-varying nature of the problem, a low-complexity algorithm is desirable, while the accuracy of the solution may only increase slowly over time. We put forth the proximal online gradient descent (OGD) algorithm for tracking the optimum of a composite objective function comprising of a differentiable loss function and a non-differentiable regularizer. An online learning framework is considered and the gradient of the loss function is allowed to be erroneous. Both, the gradient error as well as the dynamics of the function optimum or target are adversarial and the performance of the inexact proximal OGD is characterized in terms of its dynamic regret, expressed in terms of the cumulative error and path length of the target. The proposed inexact proximal OGD is generalized for application to large-scale problems where the loss function has a finite sum structure. In such cases, evaluation of the full gradient may not be viable and a variance reduced version is proposed that allows the component functions to be sub-sampled. The efficacy of the proposed algorithms is tested on the problem of formation control in robotics and on the dynamic foreground-background separation problem in video.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Online Learning With Inexact Proximal Online Gradient Descent Algorithms

Dixit

Bedi

Tripathi

et al. 2019

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…By analyzing the train braking model, it can be seen that the relationship between conditional mathematical expectation 􏽢 Q(θ, θ k ) and train braking parameters is nonlinear and nonconvex, and it is difficult to obtain its closed solution. erefore, the gradient descent optimization [28] is used to find θ which maximizes 􏽢 Q(θ, θ k ), where θ represents a set of unknown braking parameters, and the partial derivative of 􏽢 Q(θ, θ k ) with respect to parameter θ is as follows:…”

Section: Maximization Of Conditional Expectations Of High-speed Trainmentioning

confidence: 99%

High-Speed Train Emergency Brake Modeling and Online Identification of Time-Varying Parameters

Jin

Xie

Pang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

By analyzing the mechanism of pure air emergency brake for high-speed train, the discrete emergency brake model is established. Aiming at the problem that time-varying hidden parameters cannot be observed directly, the sliding window-based expectation maximization is proposed, and the unobserved time-varying brake parameters are identified. Firstly, the position and size of the sliding window are selected; then, the sliding window-based expectation maximization is used for brake parameter identification; finally, combined with the gradient optimization, the optimal identifications of emergency brake parameters are obtained. The simulation results show that the brake parameters can be identified quickly and accurately by the proposed method. Under uniform noise, the identification errors of friction coefficient and braking ratio are ±0.0068 and ±0.0349, respectively, and the maximum relative errors between the identifications and true values are 2.4807% and 1.3154%, respectively, which can meet the actual requirements of the brake system. The effectiveness and practicability of the proposed model and method are verified.

show abstract

“…The gradient descent method [12] is applied to the training of BP networks. In each iteration, the weight and offset are optimized according to the objective function (3), thereby improving the accuracy of the load prediction.…”

Section: A Bp Network Modelingmentioning

confidence: 99%

Revisit Neural Network based Load Forecasting

Tao

Zhao

Yuan

et al. 2019

2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)

View full text Add to dashboard Cite

The application of artificial neural network to load forecasting can overcome the problem of dynamic load change, and its ability to adapt to nonlinear relationships makes the prediction result satisfactory. This paper firstly reviews and introduces the concepts and basic principles of load prediction, discusses various methods for load forecasting, and then selects artificial neural network to establish a predictive model. In this paper, the European electric load is predicted with a BP neural network. From the prediction results, it is feasible to use BP neural network for load forecasting, and its accuracy can meet the needs of real-life engineering work. However, BP neural networks have the problem of slow convergence and easily falling into local minimum points. Therefore, this paper also uses three other neural networks for load forecasting, which are Radial Basis Network (RBF), Elman Network, and Long-Short Term Memory Network (LSTM). In the experiment, the four neural networks achieved expected prediction results, and the LSTM network had the best prediction effect. Scientific discussions are offered.

show abstract

Tracking Moving Agents via Inexact Online Gradient Descent Algorithm

Cited by 37 publications

References 36 publications

Online Learning With Inexact Proximal Online Gradient Descent Algorithms

Online Learning With Inexact Proximal Online Gradient Descent Algorithms

High-Speed Train Emergency Brake Modeling and Online Identification of Time-Varying Parameters

Revisit Neural Network based Load Forecasting

Contact Info

Product

Resources

About