Advertisement

Soft Computing

, Volume 22, Issue 7, pp 2381–2401 | Cite as

A novel evolutionary method of structure-diversified digital filter design and its experimental study

  • Mingguo Liu
  • Lijia Chen
  • Jingsong He
  • Peiyu Zhang
Methodologies and Application

Abstract

Digital filters are now key components in many modern digital systems. This paper proposed a novel evolutionary method to design structure-diversified digital filters. On the investigation of the existing evolution-based digital filter design methods, most state-of-the-art works concentrate on the evolution of appropriate transfer functions for digital filters. However, a transfer function is not equivalent to a practical digital filter that has proper structure and can be implemented by a hardware directly. Some researchers proposed a synthesis method to generate the structure of digital filter from a specified transfer function. However, this method needs an existing transfer function as the prior knowledge. Compared with existing works on the evolution of digital filters, the proposed method is novel at the following aspect: the proposed method can directly evolve the structure and parameters of digital filter without the pre-definition of the transfer function. The only prior knowledge we need is the specification of the design target, such as the frequency range of the passband and stop-band. In the experimental study, a significant characteristic is revealed that the proposed method is able to evolve structure-diversified filters with approximate frequency response. The proposed method is a prototype, and it is demonstrated to be a promising way of digital filter design.

Keywords

Evolution Digital filter Structure diversity Digital filter encoding 

Notes

Acknowledgements

This study was funded by the Foundation of Education Department, Henan Province, China (Grant Nos. 15A510018, 15A510019). This study was also funded by the Foundation of Technology Department, Henan Province, China (Grant No. 142102210629).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Aggarwal A, Rawat TK, Upadhyay DK (2016) Design of optimal digital FIR filters using evolutionary and swarm optimization techniques. Int J Electron Commun 70:373–385CrossRefGoogle Scholar
  2. Babayan-Mashhadi S, Lotfi R (2014) Analysis and design of a low-voltage low-power double-tail comparator. IEEE Trans Very Large Scale Integr (VLSI) Syst 22:343–352. doi: 10.1109/TVLSI.2013.2241799 CrossRefGoogle Scholar
  3. Boudjelaba K, Ros F, Chikouche D (2014) An efficient hybrid genetic algorithm to design finite impulse response filters. Expert Syst Appl 41:5917–5937. doi: 10.1016/j.eswa.2014.03.034 CrossRefGoogle Scholar
  4. Boudjelaba K, Ros F, Chikouche D (2014b) Potential of particle swarm optimization and genetic algorithms for fir filter design. Circuits Syst Signal Process. doi: 10.1007/s00034-014-9800-y Google Scholar
  5. Chandra A, Chattopadhyay S (2014) A novel approach for coefficient quantization of low-pass finite impulse response filter using differential evolution algorithm. Signal Image Video Process. doi: 10.1007/s11760-012-0359-4 Google Scholar
  6. Chen H (2002) The matrix expression of signal flow graph and its application in system analysis software. Chin J Electron 11:361–363Google Scholar
  7. Choudhary V, Ledezma E, Ayyanar R, Button RM (2008) Fault tolerant circuit topology and control method for input-series and output-parallel modular DC–DC converters. IEEE Trans Power Electron 23:402–411. doi: 10.1109/TPEL.2007.911845 CrossRefGoogle Scholar
  8. Dai C, Chen W, Zhu Y (2010) Seeker optimization algorithm for digital IIR filter design. IEEE Trans Ind Electron 57:1710–1718. doi: 10.1109/TIE.2009.2031194 Google Scholar
  9. Dhaliwal KK, Dhillon JS (2016) Integrated cat swarm optimization and differential evolution algorithm for optimal IIR filter design in multi-objective framework. Circuits Syst Signal Process. doi: 10.1007/s00034-016-0304-9 zbMATHGoogle Scholar
  10. Gold B, Jordan KL (1969) A direct search procedure for designing finite duration impulse response filter. IEEE Trans Audio Electroacoust 17:33–36CrossRefGoogle Scholar
  11. Huang C, Li G, Xu Z, Yu A, Chang L (2012) Design of optimal digital lattice filter structures based on genetic algorithm. Signal Process 92:989–998. doi: 10.1016/j.sigpro.2011.10.011 CrossRefGoogle Scholar
  12. Kar R, Mandala D, Mondal S, Ghoshal SP (2012) Craziness based particle swarm optimization algorithm for FIR band stop filter design. Swarm Evolut Comput 7:58–64. doi: 10.1016/j.swevo.2012.05.002 CrossRefGoogle Scholar
  13. Kim K-J, Wong A, Lipson H (2009) Automated synthesis of resilient and tamper-evident analog circuits without a single point of failure. Genet Program Evol Mach 11:35–59. doi: 10.1007/s10710-009-9085-2 CrossRefGoogle Scholar
  14. Li B, Wang Y, Weise T, Long L (2013) Fixed-point digital IIR filter design using two-stage ensemble evolutionary algorithm. Appl Soft Comput 13:329–338. doi: 10.1016/j.asoc.2012.09.004 CrossRefGoogle Scholar
  15. Liu M, He J (2013) An evolutionary negative-correlation framework for robust analog-circuit design under uncertain faults. IEEE Trans Evolut Comput 17:640–665. doi: 10.1109/TEVC.2012.2228208 CrossRefGoogle Scholar
  16. Lohn JD, Colombano SP (1999) A circuit representation technique for automated circuit design. IEEE Trans Evolut Comput 3(3):205–219. doi: 10.1109/4235.788491
  17. Mandal S, Ghoshal SP, Kar R, Mandal D (2012) Design of optimal linear phase FIR high pass filter using craziness based particle swarm optimization technique. J King Saud Univ Comput Inf Sci 24:83–92. doi: 10.1016/j.jksuci.2011.10.007 Google Scholar
  18. Manuel M, Elias E (2013) Design of frequency response masking FIR filter in the canonic signed digit space using modified artificial bee colony algorithm. Eng Appl Artif Intell 26:660–668. doi: 10.1016/j.engappai.2012.02.010 CrossRefGoogle Scholar
  19. Mohammed S, Mahammad SN, Kamakoti V (2011) Hardware based genetic evolution of self-adaptive arbitrary response FIR filter. Appl Soft Comput 11:842–854. doi: 10.1016/j.asoc.2010.01.004 CrossRefGoogle Scholar
  20. Mondal S, Ghoshal SP, Kar R, Mandal D (2012) Differential evolution with wavelet mutation in digital finite impulse response filter design. J Optim Theory Appl 155:315–324. doi: 10.1007/s10957-012-0028-3 MathSciNetCrossRefzbMATHGoogle Scholar
  21. Namazi A, Nourani M, Saquib M (2010) A fault-tolerant interconnect mechanism for NMR nanoarchitectures. IEEE Trans Very Large Scale Integr (VLSI) Syst 18:1433–1446. doi: 10.1109/TVLSI.2009.2024779 CrossRefGoogle Scholar
  22. Oppenheim AV, Schafer RW (2010) Discrete-time signal processing, 3rd edn. Prentice Hall, New JerseyzbMATHGoogle Scholar
  23. Pan S-T (2010) A canonic-signed-digit coded genetic algorithm for designing finite impulse response digital filte. Digital Signal Process 20:314–327. doi: 10.1016/j.dsp.2009.06.024 CrossRefGoogle Scholar
  24. Pan S (2011) Evolutionary computation on programmable robust IIR filter pole-placement design. IEEE Trans Instrumen Meas 60:1469–1479. doi: 10.1109/TIM.2010.2086850 CrossRefGoogle Scholar
  25. Parks TW, Mcclellan JH (1972) Chebyshev approximation for nonrecursive digital filters with linear phase. IEEE Trans Circuit Theory 19:189–194CrossRefGoogle Scholar
  26. Proakis J, Manolakis D (2007) Digital signal processing: principles, algorithms, and applications, 4th edn. Prentice Hall, New JerseyGoogle Scholar
  27. Rashtian H, Shirazi AHM, Mirabbasi S (2014) On the use of body biasing to improve linearity in low LO-power CMOS active mixers. Microelectron J 45:1026–1032. doi: 10.1016/j.mejo.2014.05.001 CrossRefGoogle Scholar
  28. Saha SK, Ghoshal SP, Kar R, Mandala D (2013) Cat swarm optimization algorithm for optimal linear phase FIR filter design. ISA Trans 52:781–794. doi: 10.1016/j.isatra.2013.07.009 CrossRefGoogle Scholar
  29. Salcedo-Sanz S, Cruz-Roldán F, Heneghan C, Yao X (2007) Evolutionary design of digital filters with application to subband coding and data transmission. IEEE Trans Signal Process 55:1193–1203. doi: 10.1109/TSP.2006.888883 MathSciNetCrossRefGoogle Scholar
  30. Sarangi A, Sarangi SK, Padhy SK, Panigrahi SP, Panigrahi BK (2014) Swarm intelligence based techniques for digital filter design. Appl Soft Comput. doi: 10.1016/j.asoc.2013.06.001 Google Scholar
  31. Sarkar S, Banerjee S (2014) An 8-bit low power DAC with re-used distributed binary cells architecture for reconfigurable transmitters. Microelectron J 45:666–677. doi: 10.1016/j.mejo.2014.03.014 CrossRefGoogle Scholar
  32. Sayilir S, Loke W-F, Lee J, Diamond H, Epstein B, Rhodes DL, Jung B (2014) A—90 dBm sensitivity wireless transceiver using VCO-PA-LNA-switch-modulator co-design for low power insect-basedwireless sensor networks. IEEE J Solid-State Circuits 49:996–1006. doi: 10.1109/JSSC.2013.2293022 CrossRefGoogle Scholar
  33. Shao P, Wu Z, Zhou X, Tran DC (2015) FIR digital filter design using improved particle swarm optimization based on refraction principle. Soft Comput. doi: 10.1007/s00500-015-1963-3
  34. Sharma I, Kuldeep B, Kumar A, Singh V (2016) Performance of swarm based optimization techniques for designing digital FIR filter: a comparative study. Eng Sci Technol Int J 19:1564–1572CrossRefGoogle Scholar
  35. Singh R, Verma HK (2013) Teaching learning-based optimization algorithm for parameter identification in the design of IIR filters. J Inst Eng (India): Ser B 94:285–294. doi: 10.1007/s40031-013-0063-y
  36. Sönmez Özsun S, Dündar G (2011) Simulation-based analog and RF circuit synthesis using a modified evolutionary strategies algorithm. Integr VLSI J 44:144–154. doi: 10.1016/j.vlsi.2010.11.001 CrossRefGoogle Scholar
  37. Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. Computer 27:17–26CrossRefGoogle Scholar
  38. Stanćić G, Nikolić S (2013) Digital linear phase notch filter design based on IIR all-pass filter application. Digit Signal Process 23:1065–1069. doi: 10.1016/j.dsp.2013.01.006 MathSciNetCrossRefGoogle Scholar
  39. Tan KC, Yang YJ, Goh CK (2006) A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Trans Evolut Comput 10:527–549. doi: 10.1109/TEVC.2005.860762 CrossRefGoogle Scholar
  40. Tsai J-T, Chou J-H, Liu T-K (2006) Optimal design of digital IIR filters by using hybrid taguchi genetic algorithm. IEEE Trans Ind Electron 53:867–879. doi: 10.1109/TIE.2006.874280 CrossRefGoogle Scholar
  41. Uesaka K, Kawamata M (2003) Evolutionary synthesis of digital filter structures using genetic programming. IEEE Trans Circuits Syst II: Analog Digital Signal Process 50:977–983. doi: 10.1109/TCSII.2003.820240 CrossRefGoogle Scholar
  42. Vasicek Z, Bidlo M, Sekanina L (2013) Evolution of efficient real-time non-linear image filters for fpgas. Soft Comput 17:2163–2180. doi: 10.1007/s00500-013-1040-8 CrossRefGoogle Scholar
  43. Vasundhara, Mandal D, Kar R, Ghoshal SP (2014) Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization. Nat Comput 13:55–64. doi: 10.1007/s11047-013-9381-x
  44. Wang Y, Li B, Chen Y (2011) Digital IIR filter design using multi-objective optimization evolutionary algorithm. Appl Soft Comput 11:1851–1857. doi: 10.1016/j.asoc.2010.05.034 CrossRefGoogle Scholar
  45. Wang Y, Li B, Weise T (2013) Two-stage ensemble memetic algorithm: function optimization and digital IIR filter design. Inf Sci 220:408–424. doi: 10.1016/j.ins.2012.07.041 CrossRefGoogle Scholar
  46. Xiao H, Shao Y, Zhou X, Wilcox SJ (2014) An improved simplex-based adaptive evolutionary digital filter and its application for fault detection of rolling element bearings. Measurement 55:25–32. doi: 10.1016/j.measurement.2014.04.027 CrossRefGoogle Scholar
  47. Zhang L, Morel F, Hu-Guo C, Hu Y (2014) A low-power and small-area column-level ADC for high frame-rate CMOS pixel sensor. Nuclear Instrum Methods Phys Res A 752:15–19. doi: 10.1016/j.nima.2014.03.034 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Mingguo Liu
    • 1
  • Lijia Chen
    • 1
  • Jingsong He
    • 2
  • Peiyu Zhang
    • 1
  1. 1.School of Physics and ElectronicsHenan UniversityKaifengChina
  2. 2.Department of Electronic Science and TechnologyUniversity of Science and TechnologyHefeiChina

Personalised recommendations