Abstract
This paper proposes a novel optimal design of linear phase digital band pass finite impulse response (FIR) filter using Improved Particle Swarm Optimization (IPSO) technique. IPSO is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. Evolutionary algorithms like real code genetic algorithm (RGA), PSO, IPSO have been used here for the design of linear phase band pass FIR filter. A comparison of simulation results reveals the optimization efficacy of the algorithm over the prevailing optimization techniques for the solution of the multimodal, non-differentiable, highly non-linear, and constrained filter design problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mastorakis, N.E., Gonos, I.F., Swamy, M.N.S.: Design of Two Dimensional Recursive Filters Using Genetic Algorithms. IEEE Transaction on Circuits and Systems I - Fundamental Theory and Applications 50, 634–639 (2003)
Ahmad, S.U., Antoniou, A.: A genetic algorithm approach for fractional delay FIR filters. In: IEEE International Symposium on Circuits and Systems, ISCAS 2006, pp. 2517–2520 (2006)
Lu, H.C., Tzeng, S.-T.: Design of arbitrary FIR log filters by genetic algorithm approach. Signal Processing 80, 497–505 (2000)
Chen, S.: IIR Model Identification Using Batch-Recursive Adaptive Simulated Annealing Algorithm. In: Proceedings of 6th Annual Chinese Automation and Computer Science Conference, pp. 151–155 (2000)
Karaboga, D., Horrocks, D.H., Karaboga, N., Kalinli, A.: Designing digital FIR filters using Tabu search algorithm. In: IEEE International Symposium on Circuits and Systems, ISCAS 1997, vol. 4, pp. 2236–2239 (1997)
Karaboga, N.: A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute 346(4), 328–348 (2009)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. on Neural Network (1995)
Ababneh, J.I., Bataineh, M.H.: Linear phase FIR filter design using particle swarm optimization and genetic algorithms. Digital Signal Processing 18, 657–668 (2008)
Biswal, B., Dash, P.K., Panigrahi, B.K.: Power quality disturbance classification using fuzzy C-means algorithm and adaptive particle swarm optimization. IEEE Trans. Ind. Electron. 56(1), 212–220 (2009)
Parks, T.W., McClellan, J.H.: Chebyshev approximation for non recursive digital filters with linear phase. IEEE Trans. Circuits Theory CT-19, 189–194 (1972)
Luitel, B., Venayagamoorthy, G.K.: Differential Evolution Particle Swarm Optimization for Digital Filter Design. In: 2008 IEEE Congress on Evolutionary Computation (CEC 2008), pp. 3954–3961 (2008)
Sarangi, A., Mahapatra, R.K., Panigrahi, S.P.: DEPSO and PSO-QI in digital filter design. Expert Systems with Applications 38, 10966–10973 (2011)
Mandal, D., Ghoshal, S.P., Bhattacharjee, A.K.: Application of Evolutionary Optimization Techniques for Finding the Optimal set of Concentric Circular Antenna Array. Expert Systems with Applications 38, 2942–2950 (2010)
Mandal, D., Ghoshal, S.P., Bhattacharjee, A.K.: Swarm Intelligence Based Optimal Design of Concentric Circular Antenna Array. Journal of Electrical Engineering 10(3), 30–39 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mandal, S., Ghoshal, S.P., Kar, R., Mandal, D., Shiva, S.C. (2012). Non-recursive FIR Band Pass Filter Optimization by Improved Particle Swarm Optimization. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds) Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27443-5_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-27443-5_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27442-8
Online ISBN: 978-3-642-27443-5
eBook Packages: EngineeringEngineering (R0)