Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise. (29th July 2021)

• Record Type: Journal Article
• Authors: Zhang, Zhiwei; Gao, Hongyuan; Ma, Jingya; Wang, Shihao; Sun, Helin
• Other Names: Salgotra Rohit Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-07-29
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/1496156
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.