Skip to main content

Design of Finite Impulse Response Filter with Controlled Ripple Using Cuckoo Search Algorithm

  • Conference paper
  • First Online:
Proceedings of 3rd International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1024))

Abstract

In this paper, an efficient design of finite impulse response (FIR) filter is presented with improved fitness function using cuckoo search algorithm (CSA). CSA is recently proposed evolutionary technique (ET), which has efficient ability of exploration and therefore used in FIR filter design. The fitness function is constructed in the frequency domain as a mean squared error (MSE) between the designed and desired response. In this fitness function, tolerable limits for magnitude response in passband and stopband region have been embedded, which helps in gaining the controlled ripple in irrespective bands. The designed filters are realized on general-purpose microcontroller using Arduino platform and filter performance is tested using fidelity parameters, which are; passband error (Erpb), stopband error (Ersb), and minimum stopband attenuation (As). The exhaustive experimental analysis confirms that proposed methodology is statically stable and obtains improved fidelity parameters when compared with previous state of the art.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Schlichthärle, D.: Digital Filters—Basics and Design, 2nd edn. Springer, Berlin Heidelberg (2000)

    Book  Google Scholar 

  2. Çiloǧlu, T.: An efficient local search method guided by gradient information for discrete coefficient FIR filter design. Sig. Process. 82(10), 1337–1350 (2002)

    Article  Google Scholar 

  3. Man, K.F., Tang, K.S., Kwong, S.: Genetic algorithms: concepts and applications in engineering design. IEEE Trans. Ind. Electron. 43(5), 519–534 (1996)

    Article  Google Scholar 

  4. N. Agrawal, A. Kumar, V. Bajaj, G.K. Singh, Design of bandpass and bandstop infinite impulse response filters using fractional derivative. IEEE Trans. Ind. Electron. 1–11 (2018). https://doi.org/10.1109/tie.2018.2831184

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Aggarwal, A., Rawat, T.K., Upadhyay, D.K.: Design of optimal digital FIR filters using evolutionary and swarm optimization techniques. AEU – Int. J. Electron. Commun. 70(4), 373–385 (2016)

    Article  Google Scholar 

  7. Agrawal, N., Kumar, A., Bajaj, V.: Design of digital IIR filter with low quantization error using hybrid optimization technique. Soft. Comput. 22(9), 2953–2971 (2017)

    Article  Google Scholar 

  8. Agrawal, N., Kumar, A., Bajaj, V., Singh, G.K.: High order stable infinite impulse response filter design using cuckoo search algorithm. Int. J. Autom. Comput. 14(5), 589–602 (2017)

    Article  Google Scholar 

  9. Tang, K.-S., Man, K.-F., Kwong, S., Liu, Z.-F.: Design and optimization of IIR filter structure using hierarchical genetic algorithms. IEEE Trans. Ind. Electron. 45(3), 481–487 (1998)

    Article  Google Scholar 

  10. N. Karaboga, B. Cetinkaya, Design of minimum phase digital IIR filters by using genetic algorithm, in 6th Proceedings of the Nordic Signal Processing Symposium, NORSIG 2004 (IEEE, Espoo, 2004), pp. 29–32

    Google Scholar 

  11. Yu, Y., Xinjie, Y.: Cooperative coevolutionary genetic algorithm for digital IIR filter design. IEEE Trans. Ind. Electron. 54(3), 1311–1318 (2007)

    Article  Google Scholar 

  12. Ababneh, J.I., Bataineh, M.H.: Linear phase FIR filter design using particle swarm optimization and genetic algorithms. Digit. Signal Proc. 18(4), 657–668 (2008)

    Article  Google Scholar 

  13. Sharma, I., Kuldeep, B., Kumar, A., Singh, V.K.: Performance of swarm based optimization techniques for designing digital FIR filter: a comparative study. Eng. Sci. Technol. Int. J. 19(3), 1564–1572 (2016)

    Article  Google Scholar 

  14. Karaboga, N., Kalinli, A., Karaboga, D.: Designing digital IIR filters using ant colony optimisation algorithm. Eng. Appl. Artif. Intell. 17(3), 301–309 (2004)

    Article  Google Scholar 

  15. B. Luitel, G.K. Venayagamoorthy, Differential evolution particle swarm optimization for digital filter design, in 2008 IEEE Congress on Evolutionary Computation (IEEE, Hong Kong, 2008), pp. 3954–3961

    Google Scholar 

  16. Agrawal, N., Kumar, A., Bajaj, V., Lee, H.-N.: Controlled ripple based design of digital IIR filter, in 21st IEEE International Conference on Digital Signal Processing (DSP) (IEEE, Beijing, 2016), pp. 627–631

    Google Scholar 

  17. Ahirwal, M.K., Kumar, A., Singh, G.K.: EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms. IEEE/ACM Trans. Comput. Biol. Bioinforma 10(6), 1491–1504 (2013)

    Article  Google Scholar 

  18. Agrawal, N., Kumar, A., Bajaj, V.: Digital IIR filter design with controlled ripple using cuckoo search algorithm, in 2016 International Conference on Signal and Information Processing (IConSIP) (IEEE, Vishnupuri, 2016), pp. 1–5

    Google Scholar 

  19. Kumar, M., Rawat, T.K.: Optimal fractional delay-IIR filter design using cuckoo search algorithm. ISA Trans. 59, 39–54 (2015)

    Article  Google Scholar 

  20. Gotmare, A., Patidar, R., George, N.V.: Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model. Expert Syst. Appl. 42(5), 2538–2546 (2015)

    Article  Google Scholar 

  21. Liu, G., Li, Y., He, G.: Design of digital FIR filters using differential evolution algorithm based on reserved genes, in IEEE Congress on Evolutionary Computation (IEEE, Barcelona, 2010), pp. 1–7

    Google Scholar 

  22. Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization: an overview. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  23. Rafi, S.M., Kumar, A., Singh, G.K.: An improved particle swarm optimization method for multirate filter bank design. J. Franklin Inst. 350(4), 757–769 (2013)

    Article  MathSciNet  Google Scholar 

  24. Sidhu, D.S., Dhillon, J.S., Kaur, D.: Hybrid heuristic search method for design of digital IIR filter with conflicting objectives. Soft. Comput. 21(12), 3461–3476 (2016)

    Article  Google Scholar 

  25. Gotmare, A., Bhattacharjee, S.S., Patidar, R., George, N.V.: Swarm and evolutionary computing algorithms for system identification and filter design: a comprehensive review. Swarm Evol. Comput. 32, 68–84 (2017)

    Article  Google Scholar 

  26. Agrawal, N., Kumar, A., Bajaj, V.: A new design method for stable IIR filters with nearly linear-phase response based on fractional derivative and swarm intelligence. IEEE Trans. Emerg. Top. Comput. Intell. 1(6), 464–477 (2017)

    Article  Google Scholar 

  27. Baderia, K., Kumar, A., Singh, G.K.: Hybrid method for designing digital FIR filters based on fractional derivative constraints. ISA Trans. 58, 493–508 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Dwivedi, A.K., Ghosh, S., Londhe, N.D.: Low-power FIR filter design using hybrid artificial bee colony algorithm with experimental validation over FPGA. Circuits Syst. Signal Process 36(1), 156–180 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the Department of Science and Technology, Govt. of India under Grant No. SB/S3IEECE/0249/2016.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anil Kumar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kumar, A., Agrawal, N., Sharma, I. (2020). Design of Finite Impulse Response Filter with Controlled Ripple Using Cuckoo Search Algorithm. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_37

Download citation

  • DOI: https://doi.org/10.1007/978-981-32-9291-8_37

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9290-1

  • Online ISBN: 978-981-32-9291-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics