Adaptive Differential Evolution Optimization-Based Noise-Level Measurement for High-Speed Railways

Abstract

This paper deals with the possibilities of estimating noise pollution created by high-speed railway systems in nearby locations. Railway systems have significant effects on the environment. Therefore, a college campus situated near a high-speed railway was selected as the study area. In this paper, an adaptive differential evolution optimization (ADEO) algorithm-based noise-level measurement is proposed. Various measurements such as the noise levels indoors, outdoors, and near the track were carried out in the college area and applied to ADEO for optimization. A study of the impact of railway noise on student learning was made. ADEO was used to predict the maximum noise level and the maximum noise distribution in the college area through the model. An experimental study was performed, and the results were compared with the estimated results. The results indicated the consistency of both the estimated and experimental results and the error as less than 1 dBA; the noise level exceeded 65 dBA in a few classrooms. Therefore, the proposed noise measurement for high-speed railway based on the ADEO technique has been considered as the most effective and superior optimization tool.

Actor–critic learning-based adaptive fuzzy controller for stepper motor

“…The performance of the proposed AC-TSKFC was compared with that of two benchmark controllers: (1) VRSM-FC proposed by Auday et al (2018) and (2) IHSM proposed by Yazdani et al (2018). In this study, a study of the performance indices, namely, the root means square error (RMSE) and the integral absolute error (IAE) has been made (Ganesh Babu et al, 2020). These three indices can be defined as…”