| Title |
A Neural Multiple LMS Based ANC System for Reducing Acoustic Noise of High-Speed Trains |
| Authors |
조현철(Cho, Hyun-Cheol) ; 이권순(Lee, Kwon-Soon) ; 남현도(Nam, Hyun-Do) |
| Keywords |
Active Noise Control(ANC) ; Least Mean Square(LMS) ; Neural Network ; Online Parameter Estimation ; KTX |
| Abstract |
This paper presents a novel active noise control (ANC) system using least mean square (LMS) algorithm and neural network approach for decreasing acoustic noise signals inside high-speed trains. We construct a LMS framework as a nominal ANC system and additionally design an artificial single-layered perceptron model as an auxiliary ANC which is aimed to reduce real-time residuary noise due to its nonstationary and uncertain nature. Parameter vector of the hybrid ANC is determined through online estimation to realize an adaptive ANC configuration by means of the steepest descent algorithm. We achieve simulation experiment to demonstrate the proposed ANC system employing realistic acoustic noise signals measured in Korea Train eXpress (KTX). |