Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

FIR digital filter design using improved particle swarm optimization based on refraction principle

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

An improved particle swarm optimization algorithm based on the model of refracting opposite learning, called refrPSO, is applied to design and optimize FIR low pass and high pass digital filters with linear phase. According to the refraction principle of light, the process of opposition-based learning is ameliorated, and then a new model of opposition-based learning, which is applied for improvement of particle swarm optimization, is proposed. For enhancing the performance of FIR digital filters, in this paper, the optimal combination of the filter coefficients is found out by applying the refrPSO for the design of FIR digital filters. Meanwhile, some well-known algorithms such as classic Parks-McClellan, standard Particle Swarm optimization and Particle Swarm optimization based on opposite learning are used to design FIR digital filters for comparison. Extensive experimental results show that the performance of FIR digital filters optimized by the refrPSO outperforms the one optimized by other algorithms obviously.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

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

    Article  Google Scholar 

  • Cheng P (2009) Digital Signal Processing. Ts-inghua University Press, Beijing

    Google Scholar 

  • Chu S, Tsai P (2006) Computational intelligence based on the behavior of cats. Int J Innov Comput Inf Control 3(1):163–173

    Google Scholar 

  • Dorigo M (1992) Optimization, learning and natural algorithms. Thesis Politecnico Di Milano Italy

  • Griffiths DJ (1998) Introduction to electrodynamics. Prentice Hall of India, New Delhi

    Google Scholar 

  • Jarske P, Neuvo Y, Mitra S (1988) A simple approach to the design of linear phase FIR digital filters with variable characteristics. Signal Process 14(4):313–326

    Article  MathSciNet  Google Scholar 

  • Jong K (1988) Learning with genetic algorithms: an overview. Mach Learn 3(2–3):121–138

    Google Scholar 

  • Kar R, Mandal D, Mondal S, Ghoshal S (2012) Craziness based particle swarm optimization algorithm for FIR band stop filter design. Swarm Evol Comput 7:58–64

    Article  Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neuron networks conference , p 1942–1948

  • Kumar P, Sarma GRCK, Das S, Kamalnath MAV (2013) Design of optimal digital FIR filter using particle swarm optimization algorithm. Adv Intell Syst Comput 225:187–196

    Article  Google Scholar 

  • Lee Y, Han S (2014) A minimum variance FIR filter with an H\(\infty \) error bound and its application to the current measuring circuitry. Measurement 50:115–120

    Article  Google Scholar 

  • Liu G, Li Y, He G (2010) Design of digital FIR filters using differential evolution algorithm based on reserved gene. In: IEEE congress on evolutionary computaion, p 1–7

  • Luitel B, Venayagamoorthy G (2008) Differential evolution particle swarm optimization for digital filter design. In: IEEE Congress on Evolutionary Computation, p 3954–3961

  • Malik M, Ahsan F, Mohsin S (2014) Adaptive image denoising using cuckoo algorithm. Soft Comput 1–14

  • Mandal S, Ghoshal S, Kar R (2011) FIR band stop filter optimization by improved particle swarm optimization. In: IEEE world congress on information and communication technologies 699–704

  • Mondal S, Chakraborty D, Kar R, Mandal D, Ghoshal S (2012) Novel particle swarm optimization for high pass FIR filter design. In: IEEE symposium on humanities, science and engineering research, p 413–418

  • Najjarzadeh M, Ayatollahi A (2008) FIR digital filters design: particle swarm optimization utilizing LMS and minimax strategies. In: IEEE international symposium on signal processing and information technology, p 129–132

  • Ngamtawee R, Wardkein P (2013) Linear-phase FIR design using PSO method with zero-phase pre-design. In: IEEE international conference on electrical engineering/electronics. Computer, telecommunications and information technology, p 1–5

  • Parks T, Mcclellan J (1972) Chebyshev approximation for nonrecursive digital filters with linear phase. IEEE Trans Circuit Theory 19(2):189–194

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sarangi A, Mahapatra R, Panigrahi S (2011) DEPSO and PSO-QI in digital filter design. Expert Syst Appl 38(9):10966–10973

    Article  Google Scholar 

  • Shao P, Wu Z, Zhou X (2015) Particle swarm optimization algorithm based on opposite leaning for linear phase low-pass FIR filter optimization. J Jilin Univ (Eng Technol Ed) 45(3):907–912

    Google Scholar 

  • Storn R, Price K (1997) Differential evolution a simple and effcient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MATH  Google Scholar 

  • Tizhoosh H (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation, and international conference on intelligent agents, web technologies and internet, p 695–701

  • Wang H, Li C, Liu Y, Zeng S (2007) Opposition-based particle swarm algorithm with cauchy mutation. In: IEEE congress on evolutionary computation, p 4750–4756

  • Wang H, Wu Z, Rahnamayan S, Liu Y, Ventresca M (2011a) Enhancing particle swarm optimization using generalized opposition-based learning. Inf Sci 181(20):4699–4714

    Article  MathSciNet  Google Scholar 

  • Wang Y, Wang S, Ji R (2011b) An extreme simple method for digital FIR filter design. In: IEEE international conference on measuring technology and mechatronics automation, p 410–413

  • Zhang D, Liang Y (2013) A kind of novel method of service-aware computing for uncertain mobile applications. Math Comput Model 57(3–4):344–356

    Article  Google Scholar 

  • Zhang D, Zhu Y, Zhao C (2012) A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the internet of things (IOT). Comput Math Appl 64(5):1044–1055

    Article  MATH  Google Scholar 

  • Zhang D, Li G, Zheng K (2014a) An energy-balanced routing method based on forward-aware factor for wireless sensor networks. IEEE Trans Ind Inform 10(1):766–773

    Article  Google Scholar 

  • Zhang D, Wang X, Song X (2014b) A novel approach to mapped correlation of ID for RFID anti-collision. IEEE Trans Serv Comput 7(4):741–748

    Article  Google Scholar 

  • Zhang D, Zheng K, Zhang T (2015) A novel multicast routing method with minimum transmission for WSN of cloud computing service. Soft Comput 19(7):1817–1827

    Article  Google Scholar 

  • Zhao A, Lu P, Lu J (2011) The design of FIR filter based on APA genetic algorithms. In: IEEE international conference on mechatronic science, electric engineering and computer, p 1118–1121

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No.61070008 and 61364025), and the Science and Technology Foundation of Jiangxi Province, china (No.GJJ13729).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhijian Wu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shao, P., Wu, Z., Zhou, X. et al. FIR digital filter design using improved particle swarm optimization based on refraction principle. Soft Comput 21, 2631–2642 (2017). https://doi.org/10.1007/s00500-015-1963-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-015-1963-3

Keywords

Navigation