Open AccessBigradient neural network-based quantum particle swarm optimization for blind source separation
Author(s) -
Hussein Salman,
Ali Kadhum M. Al-Qurabat,
Abd Alnasir Riyadh Finjan
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i2.pp355-364
Subject(s) - independent component analysis , particle swarm optimization , computer science , artificial neural network , blind signal separation , algorithm , noise (video) , correlation coefficient , computation , signal (programming language) , artificial intelligence , pattern recognition (psychology) , machine learning , image (mathematics) , computer network , channel (broadcasting) , programming language
An independent component analysis (ICA) is one of the solutions of a blind source separation problem. ICA is a statistical approach that depends on the statistical properties of the mixed signals. The purpose of the ICA method is to demix the mixed source signals (observation signals) and rcovering those signals. The abbreviation of the problem is that the ICA needs for optimizing by using one of the optimization approaches as swarm intelligent, neural neworks, and genetic algorithms. This paper presents a hybrid method to optimize the ICA method by using the quantum particle swarm optimization method (QPSO) to optimize the Bigradient neural network method that applies to separate mixed signals and recover sources signals. The results of an implement this work prove that this method gave good results comparing with other methods such as the Bigradient neural network and the QPSO method, based on several evaluation measures as signal-to-noise ratio, signal-to-distortion ratio, absolute value correlation coefficient, and the computation time.