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Review and Analysis of Evolutionary Optimization-Based Techniques for FIR Filter Design

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Abstract

Recent times have witnessed a wide application of evolutionary optimization by researchers, in design of digital FIR filters, based on frequency domain specifications. A significant growth has been reported in the field of evolutionary optimization-based FIR filter design. Optimization-based techniques are used to solve the filter design problem by framing the design task as an error function which is further solved to determine the filter coefficients that satisfies the desired specifications. However, the nonlinear, non-differentiable, non-convex, multimodal nature of the associated optimization problem makes the design task quite challenging. In this regard, a number of evolutionary optimization- based techniques have been applied for FIR filter design. This paper provides a comprehensive review of the various evolutionary optimization-based techniques for FIR filter design. In addition to the review, the reported techniques have been analyzed by implementing them on a common platform and comparing them in terms of their effectiveness in meeting the desired specifications.

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References

  1. J.I. Ababneh, M.H. Bataineh, Linear phase FIR filter design using particle swarm optimization and genetic algorithms. Digit. Signal Process. 18, 657–668 (2008). https://doi.org/10.1016/j.dsp.2007.05.011

    Article  Google Scholar 

  2. A. Aggarwal, T.K. Rawat, M. Kumar, D.K. Upadhyay, Design of optimal band-stop FIR filter using L1 norm based RCGA, Ain Shams Eng. J. (2016) Article in press. https://doi.org/10.1016/j.asej.2015.11.022

  3. A. Aggarwal, T.K. Rawat, D.K. Upadhyay, Design of optimal digital FIR filters using evolutionary and swarm optimization techniques. AEU Int. J. Electron. Commun. 70, 373–385 (2015). https://doi.org/10.1016/j.aeue.2015.12.012

    Article  Google Scholar 

  4. A. Aggarwal, M. Kumar, T.K. Rawat, D.K. Upadhyay, Optimal design of 2D FIR filters with quadrantally symmetric properties using fractional derivative constraints. Circuits Syst. Signal Process. 35, 2213–2257 (2016). https://doi.org/10.1007/s00034-016-0283-x

    Article  MATH  Google Scholar 

  5. M.K. Ahirwal, A. Kumar, G.K. Singh, Adaptive filtering of EEG/ERP through bounded range artificial bee colony (BR-ABC) algorithm. Digit. Signal Process. A Rev. J. 25, 164–172 (2014). https://doi.org/10.1016/j.dsp.2013.10.019

    Article  Google Scholar 

  6. S.U. Ahmad, A. Antoniou, A genetic algorithm approach for fractional delay FIR filters, in IEEE International Symposium on Circuits and Systems (IEEE, 2006) p. 4. https://doi.org/10.1109/ISCAS.2006.1693135

  7. I. Ahmad, P. Mondal, R. Kanhirodan, A new FIR filter for image restoration, in 1ST IEEE Conference Industrial Electronics Applications (IEEE, 2006) pp. 1–6. https://doi.org/10.1109/ICIEA.2006.257087

  8. C.K. Ahn, A new solution to the induced l \(\infty \) finite impulse response filtering problem based on two matrix inequalities. Int. J. Control 87, 404–409 (2014). https://doi.org/10.1080/00207179.2013.836284

    Article  MathSciNet  MATH  Google Scholar 

  9. C.K. Ahn, P. Shi, S.H. You, A new approach on design of a digital phase-locked loop. IEEE Signal Process. Lett. 23, 600–604 (2016). https://doi.org/10.1109/LSP.2016.2542291

    Article  Google Scholar 

  10. C.K. Ahn, P. Shi, M.V. Basin, M.V. Basin, Deadbeat dissipative FIR filtering. IEEE Trans. Circuits Syst. I Regul. Pap. 63, 1210–1221 (2016). https://doi.org/10.1109/TCSI.2016.2573281

    Article  MathSciNet  Google Scholar 

  11. K. Baderia, A. Kumar, G.Kumar Singh, Hybrid method for designing digital FIR filters based on fractional derivative constraints. ISA Trans. 58, 493–508 (2015). https://doi.org/10.1016/j.isatra.2015.05.015

    Article  Google Scholar 

  12. K. Boudjelaba, F. Ros, D. Chikouche, Potential of particle swarm optimization and genetic algorithms for FIR filter design. Circuits Syst. Signal Process. 33, 3195–3222 (2014). https://doi.org/10.1007/s00034-014-9800-y

    Article  Google Scholar 

  13. K. Boudjelaba, F. Ros, D. Chikouche, Adaptive genetic algorithm-based approach to improve the synthesis of two-dimensional finite impulse response filters. IET Signal Process. 8, 429–446 (2014). https://doi.org/10.1049/iet-spr.2013.0005

    Article  Google Scholar 

  14. C.S. Burrus, J.A. Barreto, I.W. Selesnick, Iterative reweighted least-squares design of FIR filters. IEEE Trans. Signal Process. 42, 2926–2936 (1994). https://doi.org/10.1109/78.330353

    Article  Google Scholar 

  15. L. Cen, Ã. Ling Cen, L. Cen, A hybrid genetic algorithm for the design of FIR filters with SPoT coefficients. Signal Process. 87, 528–540 (2007). https://doi.org/10.1016/j.sigpro.2006.06.015

    Article  MATH  Google Scholar 

  16. A. Chandra, S. Chattopadhyay, Performance analysis of DE-optimized multiplier-less finite impulse response data transmission filter, in 5th International Conference on Computer Devices for Communication (IEEE, 2012), pp. 1–4. https://doi.org/10.1109/CODEC.2012.6509216

  17. A. Chandra, S. Chattopadhyay, A new strategy of image denoising using multiplier-less FIR filter designed with the aid of differential evolution algorithm. Multimed. Tools Appl. 75, 1079–1098 (2016). https://doi.org/10.1007/s11042-014-2358-7

    Article  Google Scholar 

  18. A. Chandra, S. Chattopadhyay, B. Ghosh, Design and implementation of SORIGA-optimized powers-of-two FIR Filter on FPGA. AASRI Procedia 9, 51–56 (2014). https://doi.org/10.1016/j.aasri.2014.09.010

    Article  Google Scholar 

  19. S. Chattopadhyay, S.K. Sanyal, A. Chandra, Optimization of control parameters of differential evolution technique for the design of FIR pulse-shaping filter in QPSK modulated system. J. Commun. 6, 558–570 (2011). https://doi.org/10.4304/jcm.6.7.558-570

    Article  Google Scholar 

  20. A.G. Constantinides, W.M. Li, An algebraic approach to the estimation of the order of FIR filters from complete and partial magnitude and phase specifications. IEEE Trans. Signal Process. 55, 1213–1222 (2007). https://doi.org/10.1109/tsp.206.887565

    Article  MathSciNet  MATH  Google Scholar 

  21. M.A.G. Correa, E. Laciar, Noise removal from EEG signals in polisomnographic records applying adaptive filters in cascade, in Adaptive Filter Application (InTech, 2011) pp. 173–196. https://doi.org/10.5772/17219

  22. S. Dhabal, P. Venkateswaran, A novel accelerated artificial bee colony algorithm for optimal design of two dimensional FIR filter. Multidimens. Syst. Signal Process. 1, 1–23 (2015). https://doi.org/10.1007/s11045-015-0352-5

    MATH  Google Scholar 

  23. V. Durbadal, M. Rajib, D. Vasundhara, R. Mandal, S.P.Ghoshal Kar, Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization. Nat. Comput. 13, 55–64 (2014). https://doi.org/10.1007/s11047-013-9381-x

    Article  MathSciNet  Google Scholar 

  24. A. Dwivedi, S. Ghosh, N. Londhe, A Modified artificial bee colony optimization based FIR filter design with experimental validation using FPGA. IET Signal Process. (2016). https://doi.org/10.1049/iet-spr.2015.0214

  25. A.K. Dwivedi, S. Ghosh, N.D. Londhe, Bit level FIR filter optimization using hybrid artificial bee colony algorithm, in Annual IEEE India Conference (IEEE, 2015) pp. 1–6. https://doi.org/10.1109/INDICON.2015.7443741

  26. A.K. Dwivedi, S. Ghosh, N.D. Londhe, Low-power FIR filter design using hybrid artificial bee colony algorithm with experimental validation over FPGA, Circuits Syst. Signal Process. 1–31 (2016). https://doi.org/10.1007/s00034-016-0297-4

  27. A.K. Dwivedi, S. Ghosh, N.D. Londhe, Low power 2D finite impulse response filter design using modified artificial bee colony algorithm with experimental validation using field-programmable gate array. IET Sci. Meas. Technol. 10, 671–678 (2016). https://doi.org/10.1049/iet-smt.2016.0069

    Article  Google Scholar 

  28. A.K. Dwivedi, S. Ghosh, N.D. Londhe, Low power FIR filter design using modified multi-objective artificial bee colony algorithm. Eng. Appl. Artif. Intell. 55, 58–69 (2016). https://doi.org/10.1016/j.engappai.2016.06.006

    Article  Google Scholar 

  29. B. Elkarami, M. Ahmadi, An efficient design of 2-D FIR digital filters by using singular value decomposition and genetic algorithm with canonical signed digit (CSD) coefficients, in IEEE 54th International IEEE Midwest Symposium on Circuits and Systems (IEEE, 2011) pp. 1–4. https://doi.org/10.1109/MWSCAS.2011.6026659

  30. O. Franzen, H. Blume, H. Schröder, H. Schro, FIR-filter design with spatial and frequency design constraints using evolution strategies. Signal Process. 68, 295–306 (1998). https://doi.org/10.1016/S0165-1684(98)00079-6

    Article  MATH  Google Scholar 

  31. S.P. Ghoshal, S.K. Saha, R. Kar, D. Mandal, Seeker optimisation algorithm: application to the design of linear phase finite impulse response filter. IET Signal Process 6, 763–771 (2012). https://doi.org/10.1049/iet-spr.2011.0353

    Article  MathSciNet  Google Scholar 

  32. N. Haridas, E. Elias, Efficient variable bandwidth filters for digital hearing aid using Farrow structure. J. Adv. Res. 7, 255–262 (2016). https://doi.org/10.1016/j.jare.2015.06.002

    Article  Google Scholar 

  33. J. Hua, W. Kuang, Z. Gao, L. Meng, Z. Xu, Image denoising using 2-D FIR filters designed with DEPSO. Multimed. Tools Appl. 69, 157–169 (2014). https://doi.org/10.1007/s11042-012-1263-1

    Article  Google Scholar 

  34. A. Jiang, H.K. Kwan, Y. Zhu, X. Liu, N. Xu, Y. Tang, Design of sparse FIR filters with joint optimization of sparsity and filter order. IEEE Trans. Circuits Syst. I Regul. Pap. 62, 195–204 (2015). https://doi.org/10.1109/TCSI.2014.2354771

    Article  Google Scholar 

  35. N. Karaboga, B. Cetinkaya, Design of digital FIR filters using differential evolution algorithm. Circuits Syst. Signal Process. 25, 649–660 (2006). https://doi.org/10.1007/s00034-005-0721-7

    Article  MathSciNet  MATH  Google Scholar 

  36. S. Kockanat, N. Karaboga, T. Koza, Image denoising with 2-D FIR filter by using artificial bee colony algorithm, in International Symposium on Innovations in Intelligent Systems and Applications (IEEE, 2012) pp. 1–4. https://doi.org/10.1109/INISTA.2012.6247041

  37. S. Kockanat, N. Karaboga, The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature. Artif. Intell. Rev. 44, 265–287 (2015). https://doi.org/10.1007/s10462-014-9427-1

    Article  Google Scholar 

  38. S. Kockanat, N. Karaboga, A novel 2D-ABC adaptive filter algorithm: a comparative study. Digit. Signal Process. 40, 140–153 (2015). https://doi.org/10.1016/j.dsp.2015.02.010

    Article  MathSciNet  Google Scholar 

  39. S. Kockanat, N. Karaboga, Medical Image Denoising Using Metaheuristics (Springer, Berlin, 2017). https://doi.org/10.1007/978-3-662-54428-0

    Book  Google Scholar 

  40. T. Kondoh, Y. Nakamura, M. Nishikawa, H. Osawa, Y. Okamoto, Y. Kanai, H. Muraoka, A study on optimal BAR in array head reading, IEEE Trans. Magn. 1–1 (2017). https://doi.org/10.1109/TMAG.2017.2701355

  41. S.R. Kotha, S. Vij, S.K. Sahoo, A study on strategies and Mutant factor in differential evolution algorithm for FIR filter design, in International Conference on Signal Processing and Integrated Networks (IEEE, 2014) pp. 50–55. https://doi.org/10.1109/SPIN.2014.6776920

  42. W. Kuang, J. Hua, Z. Zheng, L. Meng, X. Zhijiang, Frequency sampling design of 2-D FIR filters based on DEPSO, in 8th International Conference on Computing (IEEE, 2012) pp. 1119–1122. https://doi.org/10.1109/ICNC.2012.6234611

  43. F. Latifoğlu, A novel approach to speckle noise filtering based on artificial bee colony algorithm: an ultrasound image application. Comput. Methods Programs Biomed. 111, 561–569 (2013). https://doi.org/10.1016/j.cmpb.2013.05.009

    Article  Google Scholar 

  44. A. Lee, M. Ahmadi, G. Jullien, W.C. Miller, R.S. Lashkari, Digital filter design using genetic algorithm, in IEEE Symposium on Advances in Digital Filtering and Signal Processing Symposium Proceedings (Cat. No.98EX185) (IEEE, 1998) pp. 34–38. https://doi.org/10.1109/ADFSP.1998.685690

  45. G.L.G. Liu, Y.L.Y. Li, G.H.G. He, Design of digital FIR filters using differential evolution algorithm based on reserved genes, in IEEE Congress Evolutionary Computation (IEEE, 2010) pp. 1–7. https://doi.org/10.1109/CEC.2010.5586425

  46. W.-S. Lu, A. Antoniou, Design of digital filters and filter banks by optimization: a state of the art review, in Proceedings of the European Signal Processing Conference, pp. 1–4 (2000)

  47. H.C. Lu, S.T. Tzeng, Design of arbitrary FIR log filters by genetic algorithm approach. Signal Process. 80, 497–505 (2000). https://doi.org/10.1016/S0165-1684(99)00146-2

    Article  MATH  Google Scholar 

  48. B. Luitel, G.K. Venayagamoorthy, Differential evolution particle swarm optimization for digital filter design, in IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence, 2008) pp. 3954–3961. https://doi.org/10.1109/CEC.2008.4631335

  49. S. Mandal, S.P. Ghoshal, R. Kar, D. Mandal, 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 (2012). https://doi.org/10.1016/j.jksuci.2011.10.007

    Google Scholar 

  50. M. Manosas-Caballu, G. Seco-Granados, A.L. Swindlehurst, Robust beamforming via FIR filtering for GNSS multipath mitigation, in IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2013), pp. 4173–4177. https://doi.org/10.1109/ICASSP.2013.6638445

  51. M. Manuel, E. Elias, Design of multiplier-less FRM FIR filter using artificial bee colony algorithm, in 20th European Conference on Circuit Theory and Design, 5, 322–325 (2011). https://doi.org/10.1109/ECCTD.2011.6043351

  52. M. Manuel, E. Elias, Design of sharp 2D multiplier-less circularly symmetric FIR filter using harmony search algorithm and frequency transformation. J. Signal Inf. Process. 3, 344–351 (2012). https://doi.org/10.4236/jsip.2012.33044

    Google Scholar 

  53. L. Martinez, B. Sarens, C. Glorieux, 2D Finite impulse response filters for surface wave identification, in IEEE International Ultrasonics Symposium (IEEE, 2009) pp. 1598–1601. https://doi.org/10.1109/ULTSYM.2009.5441443

  54. M. Mehendale, M. Road, G. Venkatesh, S.D. Sherlekar, G. Venkatesh, Coefficient optimization for low power realization of FIR filters. VLSI Signal Process. 8, 352–361 (1995). https://doi.org/10.1109/VLSISP.1995.527506

    Article  Google Scholar 

  55. A. Mehrnia, A.N. Willson, FIR filter design using optimal factoring: a walkthrough and summary of benefits. IEEE Circuits Syst. Mag. 16, 8–21 (2016). https://doi.org/10.1109/MCAS.2015.2510178

    Article  Google Scholar 

  56. D. Misra, S. Deb, S. Joardar, Efficient design of quadrature mirror filter bank for audio signal processing using Craziness based Particle Swarm Optimization Technique (2015). https://doi.org/10.1109/IC4.2015.7375563

  57. L. Mitiche, A.B.H. Adamou-Mitiche, An efficient low order model for two-dimensional digital systems: application to the 2D digital filters. J. King Saud. Univ. Comput. Inf. Sci. 26, 308–318 (2014). https://doi.org/10.1016/j.jksuci.2014.03.003

    Google Scholar 

  58. S. Mondal, D. Chakraborty, R. Kar, D. Mandal, S.P. Ghoshal, Novel particle swarm optimization for high pass FIR filter design, in IEEE Symposium on Humanities Science Engneering Research (IEEE, 2012) pp. 413–418. https://doi.org/10.1109/SHUSER.2012.6268874

  59. S. Mondal, S. Prasad, R. Kar, D. Mandal, S.P. Ghoshal, R. Kar, D. Mandal, Differential evolution with wavelet mutation in digital finite impulse response filter design. J. Optim. Theory Appl. 155, 315–324 (2012). https://doi.org/10.1007/s10957-012-0028-3

    Article  MathSciNet  MATH  Google Scholar 

  60. W.A. Mousa, S. Boussakta, D. McLernon, Realization of 2-D seismic migration FIR digital filters for 3-D seismic volumes via singular value decomposition, in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2006) p. II-801–II-804. https://doi.org/10.1109/ICASSP.2006.1660464

  61. M. Najjarzadeh, A. Ayatollahi, FIR digital filters design: particle swarm optimization utilizing LMS and minimax strategies, in IEEE International Symposium on Signal Processing and Information Technology (IEEE, 2008), pp. 129–132. https://doi.org/10.1109/ISSPIT.2008.4775685

  62. S. Narieda, IF signal filtering techniques in low IF receiver for narrowband communications, in IEEE Radio and Wireless Symposium (IEEE, 2013), pp. 94–96. https://doi.org/10.1109/RWS.2013.6486652

  63. S.-H. Ou, K.-C. Chang, C.-W. Liu, An energy-efficient, high-precision SFP LPFIR filter engine for digital hearing aids. Integr. VLSI J. 48, 230–238 (2015). https://doi.org/10.1016/j.vlsi.2014.06.004

    Article  Google Scholar 

  64. M. Pashaian, M.R. Mosavi, M.S. Moghaddasi, M.J. Rezaei, A novel interference rejection method for GPS receivers. Iran. J. Electr. Electron. Eng. 12, 9–20 (2016). https://doi.org/10.22068/IJEEE.12.1.9

  65. J. Radecki, J. Konrad, E. Dubois, Design of multidimensional finite-wordlength FIR and IIR filters by simulated annealing, IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 42, 424–431 (1995). https://doi.org/10.1109/82.392318

    Article  MATH  Google Scholar 

  66. E. Rashedi, A. Zarezadeh, Noise filtering in ultrasound images using gravitational search algorithm, in Iranian Conference on Intelligent Systems (IEEE, 2014), pp. 1–4. https://doi.org/10.1109/IranianCIS.2014.6802559

  67. K.S. Reddy, S.K. Sahoo, An approach for FIR filter coefficient optimization using differential evolution algorithm. AEU Int. J. Electron. Commun. 69, 101–108 (2015). https://doi.org/10.1016/j.aeue.2014.07.019

    Article  Google Scholar 

  68. S.K. Saha, S. Mukherjee, D. Mandal, R. Kar, S.P. Ghoshal, Gravitational search algorithm in digital FIR low pass filter design, in 3rd International Conference on Emerging Applications of Information Technology (2012, 52–55). https://doi.org/10.1109/EAIT.2012.6407860

  69. S.K. Saha, R. Kar, D. Mandal, S.P. Ghoshal, Bacteria foraging optimisation algorithm for optimal FIR filter design. Int. J. Bio-Inspired Comput. 5, 52 (2013). https://doi.org/10.1504/IJBIC.2013.053039

    Article  Google Scholar 

  70. S.K. Saha, S.P. Ghoshal, R. Kar, D. Mandal, S. Kumar, S. Prasad, R. Kar, D. Mandal, S.K. Saha, S.P. Ghoshal, R. Kar, D. Mandal, Cat Swarm Optimization algorithm for optimal linear phase FIR filter design. ISA Trans. 52, 781–794 (2013). https://doi.org/10.1016/j.isatra.2013.07.009

    Article  Google Scholar 

  71. S.K. Saha, R. Dutta, R. Choudhury, R. Kar, D. Mandal, S.P. Ghoshal, Efficient and accurate optimal linear phase FIR filter design using opposition-based harmony search algorithm. Sci. World J. 2013, 1–16 (2013). https://doi.org/10.1155/2013/320489

    Article  Google Scholar 

  72. S. Salcedo-Sanz, F. Cruz-Roldan, C. Heneghan, X.Y.X. Yao, Evolutionary design of digital filters with application to subband coding and data transmission. IEEE Trans. Signal Process. 55, 1193–1203 (2007). https://doi.org/10.1109/TSP.2006.888883

    Article  MathSciNet  MATH  Google Scholar 

  73. P. Shao, Z. Wu, X. Zhou, D.C. Tran, FIR digital filter design using improved particle swarm optimization based on refraction principle. Soft Comput. (2015). https://doi.org/10.1007/s00500-015-1963-3

  74. M. Shukla, G.R. Mishra, O.P. Singh, S. Kumar, Linear phase digital low pass FIR filter design by attractive and repulsive particle swarm optimization. ACEEE Int. J. Commun. 5, 13–19 (2014)

    Google Scholar 

  75. A.E. Smith, Multi-objective optimization using evolutionary algorithms (Book review). IEEE Trans. Evol. Comput. 6, 526–526 (2002). https://doi.org/10.1109/TEVC.2002.804322

    Article  Google Scholar 

  76. T.J. Su, C.H. Kuo, W.P. Tsai, C.C. Hou, A hybrid of clonal selection algorithm and frequency sampling method for designing a 2-D FIR filter, in Proceedings of the 4th IEEE International Symposium on Electronic Design, Test and Applications, DELTA 2008 (IEEE, 2008) pp. 274–278. https://doi.org/10.1109/DELTA.2008.15

  77. D. Suckley, Genetic algorithm in the design of FIR filters. IEE Proc. Circuits Devices Syst. 138, 234 (1991). https://doi.org/10.1049/ip-g-2.1991.0043

    Article  Google Scholar 

  78. R. Suzutou, Y. Nakamura, M. Nishikawa, H. Osawa, Y. Okamoto, Y. Kanai, H. Muraoka, A study on relationship between recording pattern and decoding reliability in SMR. IEEE Trans. Magn. 1, 1–1 (2017). https://doi.org/10.1109/TMAG.2017.2721428

    Article  Google Scholar 

  79. V. Tirronen, F. Neri, K. Tommi, K. Majava, T. Rossi, An enhanced memetic differential evolution in filter design for defect detection. Evol. Comput. 16, 529–555 (2008). https://doi.org/10.1162/evco.2008.16.4.529

    Article  Google Scholar 

  80. S.T. Tzeng, Genetic algorithm approach for designing 2-D FIR digital filters with 2-D symmetric properties. Signal Process. 84, 1883–1893 (2004). https://doi.org/10.1016/j.sigpro.2004.06.018

    Article  MATH  Google Scholar 

  81. S.T. Tzeng, Design of 2-D FIR digital filters with specified magnitude and group delay responses by GA approach. Signal Process. 87, 2036–2044 (2007). https://doi.org/10.1016/j.sigpro.2007.01.034

    Article  MATH  Google Scholar 

  82. G. Wade, A. Roberts, G. Williams, Multiplier-less FIR filter design using a genetic algorithm. IEE Proc. Vis. Image Signal Process. 141, 175 (1994). https://doi.org/10.1049/ip-vis:19941185

    Article  Google Scholar 

  83. D. Wei, C.K. Sestok, A.V. Oppenheim, Sparse filter design under a quadratic constraint: low-complexity algorithms. IEEE Trans. Signal Process. 61, 857–870 (2013). https://doi.org/10.1109/TSP.2012.2229996

    Article  MathSciNet  MATH  Google Scholar 

  84. W. Ye, Y.J. Yu, Greedy algorithm for the design of linear-phase FIR filters with sparse coefficients. Circuits Syst. Signal Process. 35, 1427–1436 (2016). https://doi.org/10.1007/s00034-015-0122-5

    Article  MathSciNet  Google Scholar 

  85. S. Zhao, Y.S. Shmaliy, F. Liu, Fast Kalman-like optimal unbiased FIR filtering with applications. IEEE Trans. Signal Process. 64, 2284–2297 (2016). https://doi.org/10.1109/TSP.2016.2516960

    Article  MathSciNet  Google Scholar 

  86. R.A. Zitar, A. Al-Dmour, An evolutionary FIR filter design method, in Evolutionary Image Analysis and Signal Processing, ed. by S. Cagnoni (Springer, Berlin, 2009). https://doi.org/10.1007/978-3-642-01636-3_11

    Google Scholar 

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Dwivedi, A.K., Ghosh, S. & Londhe, N.D. Review and Analysis of Evolutionary Optimization-Based Techniques for FIR Filter Design. Circuits Syst Signal Process 37, 4409–4430 (2018). https://doi.org/10.1007/s00034-018-0772-1

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