Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization

In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular learning algorithms: incremental least mean square (ILMS) and diffusion least mean square (DLMS) have been reported for distributed estimation...

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Vydáno v:	Digital signal processing Ročník 23; číslo 4; s. 1303 - 1313
Hlavní autoři:	Majhi, Babita, Panda, Ganapati
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.07.2013
Témata:	Algorithms Contamination Digital signal processing Distributed parameter estimation IIR system identification Incremental particle swarm optimization (IPSO) Least mean squares Least mean squares algorithm Optimization Parameter estimation Particle swarm optimization Robust distributed parameter estimation Swarm intelligence Training Robust distributed parameter estimation Distributed parameter estimation Incremental particle swarm optimization (IPSO) IIR system identification Particle swarm optimization
ISSN:	1051-2004, 1095-4333
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Abstract	In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular learning algorithms: incremental least mean square (ILMS) and diffusion least mean square (DLMS) have been reported for distributed estimation using the data collected from sensor nodes. But these algorithms, being derivative based, have a tendency of providing local minima solution particularly for minimization of multimodal cost function. Hence for problems like distributed parameters estimation of IIR systems, alternative distributed algorithms are required to be developed. Keeping this in view the present paper proposes two population based incremental particle swarm optimization (IPSO) algorithms for estimation of parameters of noisy IIR systems. But the proposed IPSO algorithms provide poor performance when the measured data is contaminated with outliers in the training samples. To alleviate this problem the paper has proposed a robust distributed algorithm (RDIPSO) for IIR system identification task. The simulation results of benchmark IIR systems demonstrate that the proposed algorithms provide excellent identification performance in all cases even when the training samples are contaminated with outliers. ► Proposes a novel distributed population based learning algorithm IPSO. ► Estimates global IIR parameters using IPSO. ► Computes robust distributed IIR parameters using Wilcoxon norm.
AbstractList	In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular learning algorithms: incremental least mean square (ILMS) and diffusion least mean square (DLMS) have been reported for distributed estimation using the data collected from sensor nodes. But these algorithms, being derivative based, have a tendency of providing local minima solution particularly for minimization of multimodal cost function. Hence for problems like distributed parameters estimation of IIR systems, alternative distributed algorithms are required to be developed. Keeping this in view the present paper proposes two population based incremental particle swarm optimization (IPSO) algorithms for estimation of parameters of noisy IIR systems. But the proposed IPSO algorithms provide poor performance when the measured data is contaminated with outliers in the training samples. To alleviate this problem the paper has proposed a robust distributed algorithm (RDIPSO) for IIR system identification task. The simulation results of benchmark IIR systems demonstrate that the proposed algorithms provide excellent identification performance in all cases even when the training samples are contaminated with outliers. In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular learning algorithms: incremental least mean square (ILMS) and diffusion least mean square (DLMS) have been reported for distributed estimation using the data collected from sensor nodes. But these algorithms, being derivative based, have a tendency of providing local minima solution particularly for minimization of multimodal cost function. Hence for problems like distributed parameters estimation of IIR systems, alternative distributed algorithms are required to be developed. Keeping this in view the present paper proposes two population based incremental particle swarm optimization (IPSO) algorithms for estimation of parameters of noisy IIR systems. But the proposed IPSO algorithms provide poor performance when the measured data is contaminated with outliers in the training samples. To alleviate this problem the paper has proposed a robust distributed algorithm (RDIPSO) for IIR system identification task. The simulation results of benchmark IIR systems demonstrate that the proposed algorithms provide excellent identification performance in all cases even when the training samples are contaminated with outliers. ► Proposes a novel distributed population based learning algorithm IPSO. ► Estimates global IIR parameters using IPSO. ► Computes robust distributed IIR parameters using Wilcoxon norm.
Author	Panda, Ganapati Majhi, Babita
Author_xml	– sequence: 1 givenname: Babita surname: Majhi fullname: Majhi, Babita email: babita.majhi@gmail.com organization: Dept. of Automatic Control and System Engineering, University of Sheffield, UK – sequence: 2 givenname: Ganapati surname: Panda fullname: Panda, Ganapati email: gpanda@iitbbs.ac.in organization: School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India
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CitedBy_id	crossref_primary_10_1016_j_dsp_2018_01_011 crossref_primary_10_1016_j_swevo_2016_06_007 crossref_primary_10_1049_iet_spr_2014_0188 crossref_primary_10_4018_IJCINI_2020100102 crossref_primary_10_1049_ccs_2019_0030 crossref_primary_10_1016_j_dsp_2014_05_008 crossref_primary_10_1007_s00034_016_0370_z crossref_primary_10_1007_s12652_018_0839_7 crossref_primary_10_1016_j_eswa_2014_10_040 crossref_primary_10_1016_j_jksues_2017_11_002 crossref_primary_10_1016_j_simpat_2015_01_007 crossref_primary_10_3390_en16041706
Cites_doi	10.1109/IPDPS.2007.370434 10.1109/79.543974 10.1109/ICASSP.2004.1326696 10.1109/ACSSC.2008.5074397 10.1109/TASSP.1985.1164706 10.1109/SUPERGEN.2009.5347938 10.1109/TAC.1985.1103972 10.1016/j.micpro.2010.11.001 10.1109/IPSN.2006.244160 10.1109/EEM.2011.5953070 10.1145/1120725.1120990 10.1109/TAC.1982.1103019 10.1109/TSP.2010.2051429 10.1016/j.eswa.2010.11.037 10.1109/MSP.2002.985672 10.1109/IPSN.2005.1440896 10.1016/j.parco.2006.11.005 10.1016/j.ins.2010.08.045 10.1109/ICASSP.2001.940390 10.1109/WCSP.2009.5371429 10.1049/cp.2010.0667 10.1109/ICASSP.2007.366830 10.1109/ICNC.2008.63 10.1109/TNN.2007.904035 10.1109/SIS.2007.368026 10.1109/ICWMC.2010.33 10.1109/ICMSAO.2011.5775570 10.1016/S0165-1684(00)00101-8 10.1109/MCOM.2002.1024422 10.1109/JSAC.2005.843546 10.1109/MC.2004.93 10.1109/21.21597 10.1109/ICASSP.2009.4960216 10.1109/CAMSAP.2011.6136004 10.1109/29.17533 10.1109/TSP.2007.896034 10.1109/SUPERGEN.2009.5348246 10.1109/53.29644 10.1109/TSP.2008.917383 10.1109/CEC.2009.4983197 10.1109/ICASSP.2006.1660721 10.1016/j.parco.2010.09.003 10.1109/IPDPSW.2010.5470706 10.1109/TAC.1979.1101973 10.1016/S1389-1286(01)00302-4 10.1109/ISCAS.2007.378747
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Keywords	Robust distributed parameter estimation Distributed parameter estimation Incremental particle swarm optimization (IPSO) IIR system identification Particle swarm optimization
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Snippet	In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular...
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SubjectTerms	Algorithms Contamination Digital signal processing Distributed parameter estimation IIR system identification Incremental particle swarm optimization (IPSO) Least mean squares Least mean squares algorithm Optimization Parameter estimation Particle swarm optimization Robust distributed parameter estimation Swarm intelligence Training
Title	Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization
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