Skip to main content
Top
Published in: Neural Computing and Applications 17/2020

19-12-2019 | Original Article

A novel singular spectrum analysis-based multi-objective approach for optimal FIR filter design using artificial bee colony algorithm

Author: Fatma Latifoğlu

Published in: Neural Computing and Applications | Issue 17/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Effective filter design plays an important role in signal processing applications. Multiple parameters must be considered to control the over-frequency response of the designed filter. In this study, a novel multi-objective approach is proposed for windowing finite impulse response (FIR) filter design. The windowing FIR filters are commonly used due to its linear phase property, frequency stability and easier implementation. However, windowing method can only control the cutting frequency of filter, and it suffers from the problem of insufficient control of the transition bandwidth, pass and stop band cutoff frequencies. Therefore, the window function was optimized using a novel multi-objective artificial bee colony (ABC) algorithm based on singular spectrum analysis (SSA) to eliminate these disadvantages of the windowing method. The proposed method was compared to three other multi-objective ABC variants. Novel SSA-based multi-objective approach yielded the best performance among four approaches. The proposed multi-objective approach that uses the SSA method has a significant advantage since it does not require user experience, it is not dependent on parameters, and there is no weight determination problem. Also, it does not have sorting and pooling stages that increase the cost of calculation. The obtained results were compared with the published literature studies. The SSA-based multi-objective approach offered better alternative to other literature techniques in terms of calculating the fitness function that deals with finding the most reasonable solution considering all error terms. Finally, the performance of the designed filter was tested on electroencephalography (EEG) signal. The EEG signal was decomposed successfully into subbands using proposed filter design approach. Based on numerical results of this study, the proposed filter provided the low-pass band and stop band ripple, and high stop band attenuation value of all, while having well enough performance.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Oppenheim AV, Ronald WS, John RB (1999) Discrete-time signal processing. Prentice Hall, Upper Saddle River Oppenheim AV, Ronald WS, John RB (1999) Discrete-time signal processing. Prentice Hall, Upper Saddle River
2.
go back to reference Najarian K, Splinter R (2012) Biomedical signal and image processing. CRC Press, Boca Raton Najarian K, Splinter R (2012) Biomedical signal and image processing. CRC Press, Boca Raton
3.
go back to reference Proakis JG, Manolakis DK (1996) Digital signal processing: principles, algorithms and applications. Prentice Hall, Upper Saddle River Proakis JG, Manolakis DK (1996) Digital signal processing: principles, algorithms and applications. Prentice Hall, Upper Saddle River
4.
go back to reference Parks TW, Burrus CS (1987) Digital filter design. Wiley, New York, pp 54–83MATH Parks TW, Burrus CS (1987) Digital filter design. Wiley, New York, pp 54–83MATH
6.
go back to reference Ababneh JI, Bataineh MH (2008) Linear phase FIR filter design using particle swarm optimization and genetic algorithms. Digit Signal Proc 18:657–668CrossRef Ababneh JI, Bataineh MH (2008) Linear phase FIR filter design using particle swarm optimization and genetic algorithms. Digit Signal Proc 18:657–668CrossRef
7.
go back to reference Datar A, Jain A, Sharmac PC (2010) Design of Kaiser window based optimized prototype filter for cosine modulated filter banks. Sig Process 90:1742–1749CrossRef Datar A, Jain A, Sharmac PC (2010) Design of Kaiser window based optimized prototype filter for cosine modulated filter banks. Sig Process 90:1742–1749CrossRef
8.
go back to reference Kumar A, Singh GK, Anand RS (2011) A closed form design method for the two-channel quadrature mirror filter banks. SIViP 5:121–131CrossRef Kumar A, Singh GK, Anand RS (2011) A closed form design method for the two-channel quadrature mirror filter banks. SIViP 5:121–131CrossRef
9.
go back to reference Kumar A, Rafia SM, Singh GK (2012) A hybrid method for designing linear-phase quadrature mirror filter bank. Digit Signal Process 22:453–462MathSciNetCrossRef Kumar A, Rafia SM, Singh GK (2012) A hybrid method for designing linear-phase quadrature mirror filter bank. Digit Signal Process 22:453–462MathSciNetCrossRef
10.
go back to reference Saha SK, Ghoshal SP, Kar R, Mandal D (2013) Cat swarm optimization algorithm for optimal linear phase FIR filter design. ISA Trans 52:781–794CrossRef Saha SK, Ghoshal SP, Kar R, Mandal D (2013) Cat swarm optimization algorithm for optimal linear phase FIR filter design. ISA Trans 52:781–794CrossRef
11.
go back to reference Dwivedi AK, Ghosh S, Londhe ND (2016) Low power FIR filter design using modified multi-objective artificial bee colony algorithm. Eng Appl Artif Intell 55:58–69CrossRef Dwivedi AK, Ghosh S, Londhe ND (2016) Low power FIR filter design using modified multi-objective artificial bee colony algorithm. Eng Appl Artif Intell 55:58–69CrossRef
12.
go back to reference San-José-Revuelta LM, Arribas JI (2018) A new approach for the design of digital frequency selective FIR filters using an FPA-based algorithm. Expert Syst Appl 106:92–106CrossRef San-José-Revuelta LM, Arribas JI (2018) A new approach for the design of digital frequency selective FIR filters using an FPA-based algorithm. Expert Syst Appl 106:92–106CrossRef
13.
go back to reference Karaboga N, Cetinkaya B (2006) Design of digital FIR filters using differential evolution algorithm. Circuits Syst Signal Process 25(5):649–660MathSciNetCrossRef Karaboga N, Cetinkaya B (2006) Design of digital FIR filters using differential evolution algorithm. Circuits Syst Signal Process 25(5):649–660MathSciNetCrossRef
14.
go back to reference Singh AP (2014) Design of linear phase low pass fir filter using particle swarm optimization algorithm. Int J Comput Appl 98(3):40–44 Singh AP (2014) Design of linear phase low pass fir filter using particle swarm optimization algorithm. Int J Comput Appl 98(3):40–44
15.
go back to reference Saha SK, Kar R, Mandal D, Ghoshal SP (2013) Bacteria foraging optimisation algorithm for optimal FIR filter design. Int J Bio-Inspir Comput 5(1):52–66CrossRef Saha SK, Kar R, Mandal D, Ghoshal SP (2013) Bacteria foraging optimisation algorithm for optimal FIR filter design. Int J Bio-Inspir Comput 5(1):52–66CrossRef
16.
go back to reference Shao P, Wu Z, Zhou X, Tran DC (2017) FIR digital filter design using improved particle swarm optimization based on refraction principle. Soft Comput 21:2631–2642CrossRef Shao P, Wu Z, Zhou X, Tran DC (2017) FIR digital filter design using improved particle swarm optimization based on refraction principle. Soft Comput 21:2631–2642CrossRef
18.
go back to reference Aggarwal A, Rawat TK, Upadhyay DK (2016) Design of optimal digital FIR filters using evolutionary and swarm optimization techniques. Int J Electron Commun (AEÜ) 70:373–385CrossRef Aggarwal A, Rawat TK, Upadhyay DK (2016) Design of optimal digital FIR filters using evolutionary and swarm optimization techniques. Int J Electron Commun (AEÜ) 70:373–385CrossRef
19.
go back to reference Dasha J, Damb B, Swainca R (2017) Design of multipurpose digital FIR double-band filter using hybrid firefly differential evolution algorithm. Appl Soft Comput 59:529–545CrossRef Dasha J, Damb B, Swainca R (2017) Design of multipurpose digital FIR double-band filter using hybrid firefly differential evolution algorithm. Appl Soft Comput 59:529–545CrossRef
20.
go back to reference Raju R, Kwan HK (2017) FIR filter design using multiobjective artificial bee colony algorithm. In: IEEE 30th Canadian conference on electrical and computer engineering (CCECE) Raju R, Kwan HK (2017) FIR filter design using multiobjective artificial bee colony algorithm. In: IEEE 30th Canadian conference on electrical and computer engineering (CCECE)
21.
go back to reference Bindima T, Elias E (2017) A novel design and implementation technique for low complexity variable digital filters using multi-objective artificial bee colony optimization and a minimal spanning tree approach. Eng Appl Artif Intell 59:133–147CrossRef Bindima T, Elias E (2017) A novel design and implementation technique for low complexity variable digital filters using multi-objective artificial bee colony optimization and a minimal spanning tree approach. Eng Appl Artif Intell 59:133–147CrossRef
22.
go back to reference Dwivedi AK, Ghosh S, Londhe ND (2018) Review and analysis of evolutionary optimization-based techniques for FIR filter design. Circuits Syst Signal Process 37:4409–4430CrossRef Dwivedi AK, Ghosh S, Londhe ND (2018) Review and analysis of evolutionary optimization-based techniques for FIR filter design. Circuits Syst Signal Process 37:4409–4430CrossRef
23.
go back to reference Fonseca CM, Peter JF (1995) An overview of evolutionary algorithms in multiobjective optimization. Evol Comput 3:1–16CrossRef Fonseca CM, Peter JF (1995) An overview of evolutionary algorithms in multiobjective optimization. Evol Comput 3:1–16CrossRef
24.
go back to reference Khan SA, Shafiqur R (2013) Iterative non-deterministic algorithms in on-shore wind farm design: a brief survey. Renew Sustain Energy Rev 19:370–384CrossRef Khan SA, Shafiqur R (2013) Iterative non-deterministic algorithms in on-shore wind farm design: a brief survey. Renew Sustain Energy Rev 19:370–384CrossRef
25.
go back to reference Emmerich MTM, Deutz AH (2018) A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat Comput 17(3):585–609MathSciNetCrossRef Emmerich MTM, Deutz AH (2018) A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat Comput 17(3):585–609MathSciNetCrossRef
26.
go back to reference Akay B, Karaboğa D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142CrossRef Akay B, Karaboğa D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142CrossRef
27.
go back to reference Karaboğa D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132MathSciNetMATH Karaboğa D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132MathSciNetMATH
28.
go back to reference Golyandina N, Zhigljavsky A (2013) Singular spectrum analysis for time series. Springer, New YorkCrossRef Golyandina N, Zhigljavsky A (2013) Singular spectrum analysis for time series. Springer, New YorkCrossRef
29.
go back to reference Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey
30.
go back to reference Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Frankl Inst 346:328–348MathSciNetCrossRef Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Frankl Inst 346:328–348MathSciNetCrossRef
31.
go back to reference Akay B, Karaboga D (2019) Parameter tuning for the artificial bee colony algorithm. In: International conference on computational collective intelligence, pp 608–619 Akay B, Karaboga D (2019) Parameter tuning for the artificial bee colony algorithm. In: International conference on computational collective intelligence, pp 608–619
32.
go back to reference Ji D (2016) The application of artificial bee colony (ABC) algorithm in FIR filter design. In: 12th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD) Ji D (2016) The application of artificial bee colony (ABC) algorithm in FIR filter design. In: 12th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD)
34.
go back to reference Kemp B, Zwinderman AH, Tuk B, Kamphuisen HAC, Oberyé JJL (2000) Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185–1194CrossRef Kemp B, Zwinderman AH, Tuk B, Kamphuisen HAC, Oberyé JJL (2000) Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185–1194CrossRef
35.
go back to reference Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220CrossRef Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220CrossRef
36.
go back to reference Parks T, McClellan J (1972) Chebyshev approximation for nonrecursive digital filters with linear phase. IEEE Trans Circuit Theory 19(2):189–194CrossRef Parks T, McClellan J (1972) Chebyshev approximation for nonrecursive digital filters with linear phase. IEEE Trans Circuit Theory 19(2):189–194CrossRef
Metadata
Title
A novel singular spectrum analysis-based multi-objective approach for optimal FIR filter design using artificial bee colony algorithm
Author
Fatma Latifoğlu
Publication date
19-12-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 17/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04680-1

Other articles of this Issue 17/2020

Neural Computing and Applications 17/2020 Go to the issue

Premium Partner