Skip to main content

Advertisement

SpringerLink
Log in

L1-Norm and LMS Based Digital FIR Filters Design Using Evolutionary Algorithms

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

The suggested work in this paper involves the construction of digital filters by utilizing optimization algorithms to compute optimum filter coefficients in such a way that the designed filter's magnitude response is identical to the ideal one. The proposed work takes a nature-inspired approach to optimizing the design of 20th order linear phase finite impulse response (FIR) based low pass, high pass and band pass filters. This approach involves the cuckoo search optimization algorithm (CSA) and Grasshopper optimization algorithms (GOA) by minimizing the least mean square error function and L1-norm based ones. These GOA and CSA are used to find the best possible values for the filter coefficients. The bench mark algorithm to design the FIR filter as Parks–McClellan approach and other recently published optimization algorithms are used to prove the superiority of proposed designs. Compared with PM method, real coded genetic algorithm, Cat swarm, Particle swarm optimization and some hybrid optimization based ones, the proposed design results have been outperform. Moreover, the proposed FIR filters give the best outcome, effectively meeting the target with decreased pass band ripples and higher attenuation in the stop band with a least execution time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4.
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

This is to inform you that the material related to manuscript entitled “L1-Norm and LMS based Digital FIR Filters Design Using Evolutionary Algorithms” is available with the author. The data includes MATLAB files, Latex files. They can be supplied upon request.

References

  1. Mitra SK (2006) Digital signal processing: a computer-based approach. McGraw-Hill Higher Education, New York

    Google Scholar 

  2. Gupta V et al (2023) Adaptive Autoregressive Modeling Based ECG Signal Analysis for Health Monitoring. Optimization Methods for Engineering Problems, Apple Academic Press, pp 1–15

  3. Gupta V (2023) Application of chaos theory for arrhythmia detection in pathological databases. Int J Med Eng Inform 15(2):191–202

    Google Scholar 

  4. Gupta V, Rathi N (2010) Various objects detection using Bayesian theory. In: Proceedings of the international conference on computer applications II, Pondicherry, India, Research Publishing Services

  5. Gupta V et al (2021) Nonlinear technique-based ECG signal analysis for improved healthcare systems. In: Proceedings of international conference on communication and computational technologies: ICCCT, Springer Singapore

  6. Gupta V et al (2022) An efficient AR modelling-based electrocardiogram signal analysis for health informatics. Int J Med Eng Inform 14(1):74–89

    MathSciNet  Google Scholar 

  7. Gupta V, Mittal M, Mittal V (2022) A simplistic and novel technique for ECG signal pre-processing. IETE J Res. https://doi.org/10.1080/03772063.2022.2135622

    Article  Google Scholar 

  8. Gupta V et al (2023) Pre-processing based ECG signal analysis using emerging tools. IETE J Res. https://doi.org/10.1080/03772063.2023.2202162

    Article  Google Scholar 

  9. Gupta V et al (2022) Detection of R-peaks using fractional Fourier transform and principal component analysis. J Ambient Intelld Human Comput 13:961–972

    Article  Google Scholar 

  10. Gupta V, Mittal M, Mittal V (2022) A novel FrWT based arrhythmia detection in ECG signal using YWARA and PCA. Wirel Pers Commun 124(2):1229–1246

    Article  Google Scholar 

  11. Gupta V, Mittal M, Mittal V (2021) FrWT-PPCA-based R-peak detection for improved management of healthcare system. IETE J Res. https://doi.org/10.1080/03772063.2021.1982412

    Article  Google Scholar 

  12. Gupta V et al (2022) PCA as an effective tool for the detection of R-peaks in an ECG signal processing. Int J Syst Assur Eng Manag 13(5):2391–2403

    Article  MathSciNet  Google Scholar 

  13. Gupta V et al (2023) ECG signal analysis based on the spectrogram and spider monkey optimisation technique. J Inst Eng (India) Ser B 104(1):153–164

    Article  Google Scholar 

  14. Gupta V et al (2023) A design of bat-based optimized deep learning model for EEG signal analysis. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-15462-2

    Article  Google Scholar 

  15. Gupta V et al (2023) An adaptive optimized schizophrenia electroencephalogram disease prediction framework. Wirel Pers Commun 130(2):1191–1213

    Article  Google Scholar 

  16. Parks TW and Sidney Burrus C (1987) Digital filter design, Wiley-Interscience

  17. Boudjelaba K, Ros F, Chikouche D (2014) An efficient hybrid genetic algorithm to design finite impulse response filters. Expert Syst Appl 41(13):5917–5937

    Article  Google Scholar 

  18. Tsutsumi S, Suyama K (2014) Design of FIR filters with discrete coefficients using ant colony optimization. Electron Commun Jpn 97(4):30–37

    Article  Google Scholar 

  19. Saha SK, Ghoshal SP, Kar R, Mandal D (2013) Cat swarm optimization algorithm for optimal linear phase FIR filter design. ISA Trans 52(6):781–794

    Article  Google Scholar 

  20. Karaboga N, Cetinkaya BI (2006) Design of digital FIR filters using differential evolution algorithm. Circ Syst Signal Process 25(5):649–660

    Article  MathSciNet  Google Scholar 

  21. Mandal S et al (2012) Design of optimal linear phase FIR high pass filter using craziness based particle swarm optimization technique. J King Saud Univ-Comput Inform Sci 24(1):83–92

    Google Scholar 

  22. Oliveira HA, Petraglia A, Petraglia MR (2009) Frequency domain FIR filter design using fuzzy adaptive simulated annealing. Circ Syst Sig Process 28:899–911

    Article  Google Scholar 

  23. Sharma I et al (2016) Performance of swarm based optimization techniques for designing digital FIR filter: a comparative study. Eng Sci Technol, An Int J 19(3):1564–1572

    Article  MathSciNet  Google Scholar 

  24. Chilamkurthi DP, Tirupatipati GC, Sulochanarani J, Pamula VK (2017) Design of optimal digital FIR filters using TLBO and Jaya algorithms. In: International conference on communication and signal processing (ICCSP 2017), pp 0538–0541

  25. Archana S, Mahapatra RK, Panigrahi SP (2011) DEPSO and PSO-QI in digital filter design. Expert Syst Appl 38:10966–10973

    Article  Google Scholar 

  26. Yadav S, Kumar M, Yadav R, Kumar A (2020) A novel approach for optimal digital FIR filter design using hybrid Grey Wolf and Cuckoo search optimization. In: First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019), Springer Singapore, pp 329–343

  27. Jiang A et al (2019) Sparse FIR filter design via partial 1-norm optimization. IEEE Trans Circ Syst II Expr Briefs 67(8):1482–1486

    Google Scholar 

  28. Jayaweera AL, Pakiyarajah D, Edussooriya CU (2022) Minimax design of MD sparse FIR filters with arbitrary frequency response using SOCP. IEEE Trans Circ Syst II Expr Briefs 69(5):2403–2407

    Google Scholar 

  29. Kumar A, Kuldeep B (2012) Design of M-channel cosine modulated filter bank using modified Exponential window. J Franklin Inst 349(3):1304–1315

    Article  MathSciNet  Google Scholar 

  30. Karthick R et al (2022) Design and analysis of linear phase finite impulse response filter using water strider optimization algorithm in FPGA. Circ Syst Sig Process 41(9):5254–5282

    Article  Google Scholar 

  31. Prashanth BUV, Ahmed MR, Kounte MR (2021) Design and implementation of DA FIR filter for bio-inspired computing architecture. Int J Electr Comput Eng 11(2):1709

    Google Scholar 

  32. Reddy KS, Sahoo SK (2015) An approach for FIR filter coefficient optimization using differential evolution algorithm. AEU-Int J Electron Commun 69(1):101–108

    Article  Google Scholar 

  33. Yadav S et al (2021) A novel approach for optimal design of digital FIR filter using grasshopper optimization algorithm. ISA Trans 108:196–206

    Article  Google Scholar 

  34. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  35. Yang X-S and Deb S (2010) Engineering optimisation by cuckoo search. arXiv preprint ar Xiv:1005.2908

  36. Ababneh JI, Bataineh MH (2008) Linear phase FIR filter design using particle swarm optimization and genetic algorithms. Digital Signal Process 18(4):657–668

    Article  Google Scholar 

  37. Luitel B, Venayagamoorthy GK (2008) Differential evolution particle swarm optimization for digital filter design. In: IEEE congress on evolutionary computation (IEEE World Congress on Computational Intelligence), pp 3954–3961

Download references

Acknowledgements

Author wish to acknowledge the university authorities, Jawaharlal Nehru Technological University Kakinada (JNTUK), Kakinada, Andhra Pradesh, India, for providing facilities to carry out this research.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, University College of Engineering Kakinada, JNTUK, Kakinada, Andhra Pradesh, 533003, India

    K. Rajasekhar

Authors
  1. K. Rajasekhar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Rajasekhar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajasekhar, K. L1-Norm and LMS Based Digital FIR Filters Design Using Evolutionary Algorithms. J. Electr. Eng. Technol. 19, 753–762 (2024). https://doi.org/10.1007/s42835-023-01589-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-023-01589-7

Keywords

Search

Navigation