Blind Source Separation based on Collective Neurodynamic Optimization Approach
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- 英文论文题名：Blind Source Separation based on Collective Neurodynamic Optimization Approach
- 论文作者：Jianchao Fan ; Ye Wang ; Jianhua Zhao ; Xiang Wang ; Xinxin Wang
- 英文论文作者：Jianchao Fan ; Ye Wang ; Jianhua Zhao ; Xiang Wang ; Xinxin Wang ; Department of Ocean Remote Sensing ; National Marine Environmental Monitoring Center ; Beijing BOE Display Technology Co. Ltd.
- 年：2017
- 作者机构：Department of Ocean Remote Sensing,National Marine Environmental Monitoring Center;Beijing BOE Display Technology Co. Ltd.;
- 英文论文关键词：Blind source separation ; Particle swarm optimization ; Neurodynamic optimization ; Tensor decomposition
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TP18
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：259k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, a collective neurodynamic optimization approach is proposed to blind source separation via tensor decomposition. Tensor decompositions have a lot of success in many fields, such as blind source separation, remote sensing image processing, text mining, linear regression, and feature extraction. However, decomposition process would cost much time and usually trap into the local minima. To solve this problem, a novel collective neurodynamic optimization(CNO) approach is presented by adopting a group of recurrent neural networks(RNN) in framework of particle swarm optimization(PSO).Through iteratively improving the best position of each RNN, the global optimal solutions of tensor decomposition are found.Blind source separation experiments confirm the validity and higher performance of the proposed algorithm in comparison to the state-of-the-art algorithms.
In this paper, a collective neurodynamic optimization approach is proposed to blind source separation via tensor decomposition. Tensor decompositions have a lot of success in many fields, such as blind source separation, remote sensing image processing, text mining, linear regression, and feature extraction. However, decomposition process would cost much time and usually trap into the local minima. To solve this problem, a novel collective neurodynamic optimization(CNO) approach is presented by adopting a group of recurrent neural networks(RNN) in framework of particle swarm optimization(PSO).Through iteratively improving the best position of each RNN, the global optimal solutions of tensor decomposition are found.Blind source separation experiments confirm the validity and higher performance of the proposed algorithm in comparison to the state-of-the-art algorithms.
In this paper, a collective neurodynamic optimization approach is proposed to blind source separation via tensor decomposition. Tensor decompositions have a lot of success in many fields, such as blind source separation, remote sensing image processing, text mining, linear regression, and feature extraction. However, decomposition process would cost much time and usually trap into the local minima. To solve this problem, a novel collective neurodynamic optimization(CNO) approach is presented by adopting a group of recurrent neural networks(RNN) in framework of particle swarm optimization(PSO).Through iteratively improving the best position of each RNN, the global optimal solutions of tensor decomposition are found.Blind source separation experiments confirm the validity and higher performance of the proposed algorithm in comparison to the state-of-the-art algorithms.
引文
[1]A.Cichocki,and S.Amari,Adaptive Blind Signal and Image Processing:Learning Algorithms and Applications.Chichester:Wiley,2002.
[2]D.Nion,K.N.Mokios,N.D.Sidiropoulos,A.Potamianos,Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures,IEEE Trans.on Audio,Speech,and Language Processing,18(6):1193-1207,2010.
[3]D.Nion,A tensor framework for nonunitary joint block diagonalization,IEEE Trans.on Signal Processing,59(10):4585-4594,2011.
[4]G.X.Zhou,and A.Cichocki,Canonical polyadic decomposition based on a single mode blind source separation,IEEE Signal Processing Letters,19(8):523-526,2012.
[5]S.N.Ge,M.Han,and X.J.Hong,A fully automatic ocular artifact removal from EEG based on fourth-order tensor method,Biomeical Engineering Letter,4(1):55-63,2014.
[6]W.S.B.Ouedraogo,A.Souloumiac,M.Jaidane,C.Jutten,Non-negative blind source separation algorithm based on minimum aperture simplicial cone,IEEE Trans.on Signal Processing,62(2):376-389,2014.
[7]F.Abrard,and Y.Deville,A time frequency blind signal separation method applicable to underdetermined mixtures of dependent sources,Signal Processing,85(7):1389-1403,2005.
[8]S.Y.Kim,and C.D.Yoo,Underdetermined blind source separation based on subspace representation,IEEE Trans.on Signal Processing,57(7):2604-2614,2009.
[9]P.Comon,Blind identification and source separation in 2×3under-determined mixtures,IEEE Trans.on Signal Processing,52(7):11-22,2004.
[10]L.D.Lathauwer,J.Castaing,and J.F.Cardoso,Fourth-order cumulantbased blind identification of underdetermined mixtures,IEEE Trans.on Signal Processing,55(6):2965-2973,2007.
[11]P.Tichavsky,and Z.Koldovsky,Weight adjusted tensor method for blind separation of underdetermined mixtures of nonstationary sources,IEEE Trans.on Signal Processing,59(3):1037-1047,2011.
[12]A.Cichocki,R.Zdunek,A.H.Phan,and S.Amari,Nonegative Matrix and Tensor Factorizations:Application to Exploratory Multi-way Data Analysis and Blind Source Separation.Chichester:Wiley,2009.
[13]D.D.Lee,and H.S.Seung,Algorithms for nonnegative matrix factorization,in Proceedings of Advances in Neural Information Processing Systems,2000:556-562.
[14]Y.N.Chen,D.R.Han,and L.Q.Qi,New ALS methods with extrapolating search directions and optimal step size for complex-valued tensor decompositions,IEEE Trans.on Signal Processing,59(12):5888-5898,2011.
[15]X.B.Gao,and L.Z.Lia,A new one-Layer neural network for Linear and quadratic programming,IEEE Trans.on Neural Networks,21(6):918-929,2010.
[16]S.Das,and P.N.Suganthan,Differential evolution:A survey of the state-of-the-art.IEEE Trans.on Evolutionary Computation,15(1):4-31,2011.
[17]J.Kennedy,and R.Eberhart,Particle swarm optimization,in Proceedings of IEEE International Conference on Neural Networks,1995:1942-1948.
[18]P.Larranaga,J.A.Lozano,Estimation of Distribution Algorithm:A New Tool for Evolutionary Computation.Berlin Heidelberg:Springer,2002.
[19]J.C.Fan,and M.Han,Nonliear model predictive control of ball-plate system based on gaussian particle swarm optimization,in Proceedings of IEEE Congress on Evolutionary Computation,2012:1-6.
[20]J.C.Fan,J.Wang,and M.Han,Cooperative coevolution for large-scale optimization based on kernel fuzzy clustering and variable trust region methods,IEEE Trans.on Fuzzy Systems,22(8):829-839,2014.
[21]E.Vincent,R.Gribonval,and C.Fevotte,Performance measurement in blind audio source separation,IEEE Trans.on Audio,Speech,and Language Processing,14(4):1462-1469,2006.
[2]D.Nion,K.N.Mokios,N.D.Sidiropoulos,A.Potamianos,Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures,IEEE Trans.on Audio,Speech,and Language Processing,18(6):1193-1207,2010.
[3]D.Nion,A tensor framework for nonunitary joint block diagonalization,IEEE Trans.on Signal Processing,59(10):4585-4594,2011.
[4]G.X.Zhou,and A.Cichocki,Canonical polyadic decomposition based on a single mode blind source separation,IEEE Signal Processing Letters,19(8):523-526,2012.
[5]S.N.Ge,M.Han,and X.J.Hong,A fully automatic ocular artifact removal from EEG based on fourth-order tensor method,Biomeical Engineering Letter,4(1):55-63,2014.
[6]W.S.B.Ouedraogo,A.Souloumiac,M.Jaidane,C.Jutten,Non-negative blind source separation algorithm based on minimum aperture simplicial cone,IEEE Trans.on Signal Processing,62(2):376-389,2014.
[7]F.Abrard,and Y.Deville,A time frequency blind signal separation method applicable to underdetermined mixtures of dependent sources,Signal Processing,85(7):1389-1403,2005.
[8]S.Y.Kim,and C.D.Yoo,Underdetermined blind source separation based on subspace representation,IEEE Trans.on Signal Processing,57(7):2604-2614,2009.
[9]P.Comon,Blind identification and source separation in 2×3under-determined mixtures,IEEE Trans.on Signal Processing,52(7):11-22,2004.
[10]L.D.Lathauwer,J.Castaing,and J.F.Cardoso,Fourth-order cumulantbased blind identification of underdetermined mixtures,IEEE Trans.on Signal Processing,55(6):2965-2973,2007.
[11]P.Tichavsky,and Z.Koldovsky,Weight adjusted tensor method for blind separation of underdetermined mixtures of nonstationary sources,IEEE Trans.on Signal Processing,59(3):1037-1047,2011.
[12]A.Cichocki,R.Zdunek,A.H.Phan,and S.Amari,Nonegative Matrix and Tensor Factorizations:Application to Exploratory Multi-way Data Analysis and Blind Source Separation.Chichester:Wiley,2009.
[13]D.D.Lee,and H.S.Seung,Algorithms for nonnegative matrix factorization,in Proceedings of Advances in Neural Information Processing Systems,2000:556-562.
[14]Y.N.Chen,D.R.Han,and L.Q.Qi,New ALS methods with extrapolating search directions and optimal step size for complex-valued tensor decompositions,IEEE Trans.on Signal Processing,59(12):5888-5898,2011.
[15]X.B.Gao,and L.Z.Lia,A new one-Layer neural network for Linear and quadratic programming,IEEE Trans.on Neural Networks,21(6):918-929,2010.
[16]S.Das,and P.N.Suganthan,Differential evolution:A survey of the state-of-the-art.IEEE Trans.on Evolutionary Computation,15(1):4-31,2011.
[17]J.Kennedy,and R.Eberhart,Particle swarm optimization,in Proceedings of IEEE International Conference on Neural Networks,1995:1942-1948.
[18]P.Larranaga,J.A.Lozano,Estimation of Distribution Algorithm:A New Tool for Evolutionary Computation.Berlin Heidelberg:Springer,2002.
[19]J.C.Fan,and M.Han,Nonliear model predictive control of ball-plate system based on gaussian particle swarm optimization,in Proceedings of IEEE Congress on Evolutionary Computation,2012:1-6.
[20]J.C.Fan,J.Wang,and M.Han,Cooperative coevolution for large-scale optimization based on kernel fuzzy clustering and variable trust region methods,IEEE Trans.on Fuzzy Systems,22(8):829-839,2014.
[21]E.Vincent,R.Gribonval,and C.Fevotte,Performance measurement in blind audio source separation,IEEE Trans.on Audio,Speech,and Language Processing,14(4):1462-1469,2006.