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A Review of Comparison of Various Linear Phase FIR Filter Algorithms to Design an Optimum Filter

Mayank Sharma*, Akanksha Khedkar**, Akanksha Jangde***, Bhanu Pratap Patel****, Dharmendra Singh*****

*-**** UG Scholar, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur (C.G), India.

***** Assistant Professor, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur (C.G), India.

Periodicity:January - March'2017
DOI : https://doi.org/10.26634/jdp.5.1.13531

Abstract

For better design of the FIR filter, it is necessary for the designer to know the drawbacks of all the design methods. In this paper, the authors have compared two algorithms to get a better solution to design an optimum FIR filter. A lot of works have been already done in the design of FIR filter. So the methods and analysis of the algorithms help in the design of the filter, which at least removes all the drawbacks of both the filter design algorithms, which has been discussed in this paper. The papers based on the Parks McClellan algorithm, Particle Swarm Optimization method (PSO), Dynamic and Adjustable Particle Swarm Optimization (DAPSO), Particle Swarm Optimization with Variable Acceleration Factor (PSOVAF) in Linear Phase Digital Low Pass FIR Filter, planned Hybrid algorithm are quick and economical evolutionary algorithms, Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Differential Evolution (DE) based algorithms are used to compare them to obtain a solution. Therefore an effective and efficient optimized FIR filter can be designed.

Keywords

DAPSO, PSO-VAF, Particle Swarm Optimization (PSO), Cuckoo Search Algorithm, Harmony Search Algorithm (HSA), Firefly Algorithm, RGA, DE

How to Cite this Article?

Sharma, M., Khedkar, A., Jangde, A., Patel, B, P., Dharmendra. (2017). A Review of Comparison of Various Linear Phase FIR Filter Algorithms to Design An Optimum Filter. i-manager’s Journal on Digital Signal Processing, 5(1), 39-45. https://doi.org/10.26634/jdp.5.1.13531

References

[1]. M. Ammar, Bouaziz, S. Alimi, and A.M. Abraham, (2013). “Hybrid harmony search algorithm for global optimization”. IEEE international World Congress on Nature and Biologically Inspired Computing (NaBIC), ISBN: 9781479914142, pp. 69 75.

[2]. A. Antoniou, (1993). “Digital Filters: Analysis, Design and nd Applications”. 2 ed.McGraw Hill.

[3]. Z. Cui, L. Gao, H. Ouyang, and H. Li, (2013). “Hybrid differential evolution harmony search algorithm for numerical optimization problems”. In International conference on Control and Decision Conference (CCDC), ISBN: 978-1-4673-5533-9, pp. 2930 – 2933.

[4]. X.Z. Gao, K. Zenger, and X. Wang, (2012). “A novel Harmony Search method with dual memory”. IEEE International Conference on Systems, Man, and Cybernetics (SMC), ISBN: 978-1-4673-1712-2, pp. 177 –183.

[5]. T. Gonsalves, and A. Egashira, (2013). “Parallel Swarms Oriented Particle Swarm Optimization”. Applied Computational Intelligence and Soft Computing, Article ID 756719, pp. 7, Volume 2013.

[6]. C. Jing, W. Yamin, and L. Junqing, (2012). “A hybrid harmony search algorithm combined with differential evolution for global optimization problems”. In IEEE International Conference on Control Conference (CCC), ISSN: 19341768, pp. 2509-2513.

[7]. H. Li, and L. Li, (2007). “A Novel Hybrid Particle Swarm Optimization Algorithm Combined with Harmony Search for High Dimensional Optimization Problems”. International Conference on Intelligent Pervasive Computing, ISBN: 9780769530062, pp. 94-97.

[8]. X. Changming, and Y. Lin, (2011). “A new improved harmony search algorithm for continuous optimization problems”. In International Conference on Computer Science and Network Technology (ICCSNT), Vol. 2, pp. 686 689.

[9]. S. Prasad, S. Ghoshal, R. Kar, and D. Mandal, (2011). “Optimal linear phase finite impulse response band pass filter design using craziness based particle swarm optimization algorithm”. Journal of Shanghai Jiaotong University (Science), ISSN: 1007-1172 Vol. 16, No. 6, pp. 696-703.

[10]. S. Mandal, R. Kar, D. Mandal, and S.P. Ghoshal, (2011). “Swarm Intelligence based Optimal Linear Phase FIR High Pass Filter Design using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach”. World Academy of Science, Engineering and Technology, Vol. 5.

[11]. S. Mukherjee, R. Kar, D. Mandal, and S. Mondal, (2011). “Linear phase low pass FIR filter design using Improved Particle Swarm Optimization”. IEEE Student Conference on Research and Development (SCOReD), ISBN: 978-1-4673-0099-52011, pp. 358 – 363.

[12]. Amanjeet Panghal, Nitin Mittal, Devender Pal Singh, R.S. Chauhan, and Sandeep K. Arya, (2010). “Comparison of Various Optimization Techniques For Design Fir Digital Filters”. NCCI 2010 - National Conference on Computational Instrumentation CSIO Chandigarh, pp. 19- 20.

[13]. T.W. Parks, and J.H. McClellan, (1972). Chebyshev approximation for nonrecursive Digital Filters with Linear Phase”. IEEE Trans.Circuit Theory, Vol. 19, pp, 189-194.

[14]. Ghosal, K., Kar, R. Mandal, and D. Saha, (2013). “A Novel Firefly Algorithm for Optimal Linear Phase FIR Filter Design”. International Journal of Swarm Intelligence Research, Vol. 4, No. 2, pp. 29-48.

[15]. S.K. Saha, R. Kar, D. Mandal, and S.P. Ghoshal, (2013). “Linear infinite impulse response system identification using harmony search algorithm”. International Conference on Communications and Signal Processing (ICCSP), ISBN: 9781467348652, pp. 154 158.

[16]. M. Shukla, and G.R. Mishra, (2014). “DAPSO and PSOVAF in Linear Phase Digital Low Pass FIR Filter Design”. Circuits and Systems, Vol. 5, pp. 57-67.

[17]. A.P. Singh, and Neha, (2014). “Design of Linear Phase Low Pass FIR Filter using Particle Swarm Optimization Algorithm”. International Journal of Computer Applications, Vol. 98– No. 3.

[18]. T. Singh, and H.S. Josan, (2014). “Design of Low Pass Digital FIR Filter using Cuckoo Search Algorithm”. International Journal of Engineering Research and Applications, ISSN: 2248-9622, Vol. 4, No. 8, pp.72-77.

[19]. H.R. Tizhoosh, (2005). “Opposition-based learning: a new scheme for machine intelligence”. In Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, The Scientific World Journal, Vol. 1, pp. 695–701.

[20]. H.R. Tizhoosh, (2006). “ Opposition-based reinforcement learning”. Journal of Advanced Computational Intelligence and Intelligent Informatics, The Scientific World Journal, Vol. 10, pp. 578–585.

[21]. Vural, R.A. Turkey, and U.E. Ayten, “Optimized analog th filter approximation via evolutionary algorithms”. 12 International Conference on Intelligent Systems Design and Applications (ISDA), ISSN: 21647143, pp. 485-90.

[22]. H. Wang, H. Ouyang, L. Gao, and W. Qin, (2014). “Opposition based learning harmony search algorithm with th mutation for solving global optimization problems”. 26 International Chinese conference on Control and Decision Conference (CCDC), ISBN: 9781479937073, pp.1090- 1094.

[23]. J. Yang, and J. Zhu, (2012). “A Modified Harmony Search Algorithm for Optimization Problems”. Fifth International Symposium on Computational Intelligence and Design (ISCID), Vol. 2, pp. 100–104.

[24]. J. Duan, H. Sang, J. Li, and B. Zhang, (2014). “A new penalty function method for constrained optimization using harmony search algorithm”. IEEE Congress on Evolutionary Computation (CEC), ISBN: 9781479966264, pp. 853-859.

A Review of Comparison of Various Linear Phase FIR Filter Algorithms to Design an Optimum Filter

Abstract

Keywords

How to Cite this Article?

References

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