An Energy-Efficient Train Operation Approach by Integrating the Metro Timetabling and Eco-Driving

Adaptive Control & Signal

et al. 2020

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.

Section: Discussionmentioning

confidence: 99%

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Cui

Adaptive Control & Signal

et al. 2020

“…Compared with the F-GSG algorithm, the F-MI-GSG, and F-MI-GFG algorithms based on the data filtering technique and multiinnovation identification theory have higher parameter estimation accuracy. The proposed parameter identification algorithms for a nonlinear RBF-ARAR network in this article can be extended to other bilinear and nonlinear systems and can be applied to other areas [70][71][72][73][74] such as the information processing and transportation communication systems [75][76][77][78][79][80][81] and so on.…”

Section: Discussionmentioning

confidence: 99%

Parameter identification of a nonlinear radial basis function‐based state‐dependent autoregressive network with autoregressive noise via the filtering technique and the multiinnovation theory

Zhou

Intl J Robust & Nonlinear

et al. 2020

This article studies the parameter estimation problems of radial basis function-based state-dependent autoregressive models with autoregressive noises (RBF-ARAR models). To reduce the effect of the colored noise to parameter estimation, the data filtering technique is applied and a filtering based generalized stochastic gradient algorithm is derived for the RBF-ARAR models. In order to achieve more accurate parameter estimates, a filtering based multiinnovation generalized stochastic gradient (F-MI-GSG) algorithm is proposed by utilizing the current and past innovations. Introducing two forgetting factors, a filtering based multiinnovation generalized forgetting gradient algorithm is developed to improve the transient performance of the F-MI-GSG algorithm. The effectiveness of the proposed algorithms is verified through the simulation examples.

“…The simulation results validate the performance of the presented algorithms. The algorithm in this article is proposed for linear time-delay systems but the idea can extended to other linear and nonlinear time-delay stochastic systems [65][66][67][68][69][70][71][72][73][74][75] and can be applied to other literature studies [76][77][78][79][80][81][82][83][84][85][86][87][88] such as signal processing and vibration analysis.…”

Section: Discussionmentioning

confidence: 99%

Parameter estimation for time‐delay systems based on the frequency responses and harmonic balance methods

Adaptive Control & Signal

et al. 2020

Two parameter estimation methods for linear time-delay systems are proposed based on the frequency responses and the harmonic balance methods. One is the stochastic gradient gradient-based iterative (SG-GI) algorithm and the other is the recursive least squares gradient-based iterative (RLS-GI) algorithm. These two methods can estimate the unknown parameters and the unknown time delays simultaneously by giving sinusoidal signals with different angular frequencies. Furthermore, a comparative study reveals that the RLS-GI algorithm is more effective than the SG-GI algorithm. The effectiveness of the proposed algorithms is illustrated by a numerical example.