Novel Approach of Designing Multiplier-less Finite Impulse Response Filter using Differential Evolution Algorithm

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Author(s)

Abhijit Chandra 1,* Sudipta Chattopadhyay 2

1. Department of Electronics and Telecommunication Engineering Bengal Engineering and Science University, Shibpur, India

2. Department of Electronics and Telecommunication Engineering Jadavpur University, Kolkata, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2012.04.08

Received: 12 Aug. 2011 / Revised: 3 Nov. 2011 / Accepted: 15 Jan. 2012 / Published: 8 Apr. 2012

Index Terms

Differential Evolution (DE), Finite duration Impulse Response (FIR) filter, Multiplier-less architecture, Sum of power of two (SPT) terms, Total power of two (TPT) terms, Zero-valued filter coefficient (ZFC)

Abstract

Reduction of computational complexity of digital hardware has drawn the special attention of researchers in recent past. Proper emphasis is needed in this regard towards the settlement of computationally efficient as well as functionally competent design of digital systems. In this communication, we have made one novel attempt for designing multiplier-free Finite duration Impulse Response (FIR) digital filter using one robust evolutionary optimization technique, called Differential Evolution (DE). The search has been directed through two sequentially opposite paths which include quantization and optimization as fundamental operations. Besides performing a detailed comparative analysis between these two proposed approaches; the performance evaluation of the designed filter with other existing discrete coefficient FIR models has also been carried out. Finally, the optimum search method for realizing the required set of specifications has been suggested.

Cite This Paper

Abhijit Chandra, Sudipta Chattopadhyay, "Novel Approach of Designing Multiplier-less Finite Impulse Response Filter using Differential Evolution Algorithm", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.4, pp.54-62, 2012. DOI:10.5815/ijisa.2012.04.08

Reference

[1]Mitra S. K. Digital Signal Processing: A Computer-based Approach. McGraw-Hill, 2001.

[2]Somanathan Nair, B. Digital Signal Processing: Theory, Analysis and Digital-filter Design. Prentice-Hall, 2004.

[3]Antoniou, A. Digital Filters: Analysis, Design and Applications. McGraw-Hill, 2001.

[4]Tan, L. Digital Signal Processing: Fundamentals and Applications. Academic Press, 2008.

[5]Kaakinen, J. Y., Saramaki, T. A Systematic Algorithm for the Design of Multiplierless FIR Filters. Proceedings of the 2001 IEEE International Symposium on Circuits and Systems, 2001, 2: 185-188.

[6]Yao, C. Y. A Study of SPT-term Distribution of CSD Numbers and Its Application for Designing Fixed-point Linear Phase FIR Filters. Proceedings of the 2001 IEEE Symposium on Circuits and Systems, Australia: Sydney, 2001, 2: 301-304.

[7]Jheng, K., Jou, S., Wu, A. A Design Flow for Multiplierless Linear-Phase FIR Filters: from System Specification to Verilog Code. Proceedings of the 2004 IEEE International Symposium on Circuits and Systems, 2004, 5: 293-296.

[8]Kaakinen, J., Saramaki, T. Bregovic, R. An Algorithm for the Design of Multiplier-less Two-channel Perfect Reconstruction Orthogonal Lattice Filter Banks. Proceedings of the first International Symposium on Control, Communications and Signal Processing, 2004: 415-418.

[9]Yu, Y. J., Lim, Y. C. Signed Power-of-two Allocation Scheme for the Design of Lattice Orthogonal Filter Banks. Proceedings of the 2005 IEEE International Symposium on Circuits and Systems, Singapore: Nanyang Technological University, 2005, 2: 1819-1822.

[10]Li, D., Song, J., Lim, Y. C. A Polynomial-Time Algorithm for Designing Digital Filters with Power-of-Two Coefficients. Proceedings of the IEEE International Symposium on Circuits and Systems, 1993, 1: 84-87.

[11]Lim, Y. C., Parker, S. R. FIR Filter Design over a Discrete Powers-of-Two Coefficient Space. IEEE Transactions on Acoustic, Speech, Signal Processing, 1983, ASSP-31: 583-591. 

[12]Chen, C., Willson, A. N. A Trellis Search Algorithm for the Design of FIR Filters with Signed-Powers-of-Two Coefficients. IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, 1999, 46(1): 29-39.

[13]Chattopadhyay, S., Sanyal, S. K., Chandra, A. Design of FIR Pulse-Shaping Filter: Superiority of Differential Evolution Optimization over Convex Optimization. Proceedings of the fifth European Conference on Circuits and Systems for Communication, Serbia: Belgrade, 2010: 189-192.

[14]Suckley, D. Genetic Algorithm in the Design of FIR Filters. IEE Proceedings of Circuits, Devices and Systems, 1991, 138 (2): 234:238. 

[15]Najjarzadeh, M., Ayatollahi, A. FIR Digital Filter Design: Particle Swarm Optimization Using LMS and Minimax Strategies. Proceedings of the 2008 IEEE International Symposium on Signal Processing and Information Technology, Sarajevo, 2008: 129-132.

[16]Luitel, B., Engelbrecht, A. P. Differential Evolution Particle Swarm Optimization for Digital Filter Design. Proceedings of the IEEE World Congress on Computational Intelligence, 2008: 3954-3961.

[17]Karaboga, N. Digital Filter Design using Differential Evolution Algorithm. EURASIP Journal of Applied Signal Processing, 8, 2005: 1269-1276.

[18]Das, S., Abraham, A., Konar, A. Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives, Studies in Computational Intelligence (SCI), 2008, 116: 1-38.

[19]Storn, R., Price, K. Differential Evolution—A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. International Computer Science Institute, Berkeley, 1995: TR-95-012.

[20]Storn, R., Price, K. Differential Evolution—A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces. Journal of Global Optimization, 1997, 11(4): 341-359.

[21]Storn, R., Price, K. Lampinen, J. Differential Evolution- A Practical Approach to Global Optimization. Springer, 2005.

[22]Das, S., Suganthan, P. N. Differential Evolution: A Survey of the State-of-the-art. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 4-31.

[23]Liu, J., Lampinen, J. A Fuzzy Adaptive Differential Evolution Algorithm. Proceedings of the 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering (TENCON’02), Beijing, 2002: 606-611.

[24]Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V. Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 646-657.

[25]Rahnamayan, S., Tizhoosh, H. R., Salama, M. M. A. Opposition-based Differential Evolution. IEEE Transactions on Evolutionary Computation, 2008, 12(1): 64-79.

[26]Das, S., Abraham, A., Chakraborty, U. K., Konar, A. Differential Evolution with a Neighborhood Based Mutation Operator. IEEE Transactions on Evolutionary Computation, 2009, 13(3): 526-553.

[27]Zhang, J., Sanderson, A. C. JADE: Adaptive Differential Evolution with Optional External Archive. IEEE Transactions on Evolutionary Computation, 2009, 13(5): 945-958.