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Evolutionary synthesis of low-sensitivity digital filters using adjacency matrix

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Abstract

An evolutionary synthesis method to generate digital filters with low coefficient sensitivity is presented. The method uses a chromosome coding based on the graph adjacency matrix representation. It is shown that the proposed chromosome representation enables to easily verify and avoid the generation of topologically invalid and non-computable individuals during the evolutionary process. The efficiency of the proposed algorithm is tested in the synthesis of two low-pass digital filters and the results are compared with other examples found in the literature.

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References

  1. Oppenheim AV, Schafer RW (1975) Digital signal processing. Prentice-Hall International, London

    MATH  Google Scholar 

  2. Rosenman MA (1995) An evolutionary model for non-routine design. In: Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence, pp 363–370

  3. Yao X, Liu Y (1998) Towards designing artificial neural networks by evolution. Appl Math Comput 91:83–90

    Article  MATH  Google Scholar 

  4. Schnier T, Yao X, Liu P (2003) Digital filter design using multiple pareto fronts. Soft Comput 8:332–343

    Google Scholar 

  5. Koza JR, Bennett FH III, Andre D, Martin A, Dunlap F (1998) Automated synthesis of analog electrical circuits by means of genetic programming. IEEE Trans Evol Comput 1:109–128

    Article  Google Scholar 

  6. Somani A, Chakrabarti PP, Patra A (2007) An evolutionary algorithm-based approach to automated design of analog and RF circuits using adaptive normalized cost functions. IEEE Trans Evol Comp 11(3):336–353

    Article  Google Scholar 

  7. Chang SJ, Hou HS, Su YK (2006) Automated passive filter synthesis using a novel tree representation and genetic programming. IEEE Trans Evol Comp 10(1):93–100

    Article  Google Scholar 

  8. Mattiussi C (2005) Evolutionary synthesis of analog networks. Ph.D. dissertation, THÈSE No 3199 (2005), EPFL, Lausanne, Italy

  9. Hou HS, Chang SJ, Su YK (2005) Practical passive filter synthesis using genetic programming. IEICE Trans Electron E88-C(6):1180–1185

  10. Hou HS, Chang SJ, Su YK (2005) Economical passive filter synthesis using genetic programming based on tree representation. In: Proceedings IEEE International Symposium on Circuits System (ISCAS), IEEE Press, New York, pp 3003–3006

  11. Sá LB, Botelho JPB, Vieira PF, Mesquita A (2003) An experiment on nonlinear synthesis using evolutionary techniques based only on CMOS transistors. In: Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware, IEEE Computer Press, Chicago, pp 50–58

  12. Grimbleby JB (2000) Automatic analogue circuit synthesis using genetic algorithms. IEE Proc Circuits Devices Syst 147(6):319–323

    Article  Google Scholar 

  13. Lohn JD, Colombano SP (1999) A circuit representation technique for automated circuit design. IEEE Trans Evol Comp 3(3):205–219

    Article  Google Scholar 

  14. Cheang SM, Lee KH, Leung KS (2007) Applying genetic parallel programming to synthesize combinational logic circuits. IEEE Trans Evol Comp 11(4):503–520

    Article  Google Scholar 

  15. Stomeo E, Kalganova T, Lambert C (2006) Generalized disjunction decomposition for evolvable hardware. IEEE Trans Syst Man Cybern B Cybern 36(5):1024–1043

    Article  Google Scholar 

  16. Koza J, Bennett FH, Andre D, Keane MA (1999) Genetic programming III. Darwinian invention and problem solving. Morgan Kaufmann, San Mateo

  17. Miller JF, Job D, Vassilev K (2000) Principles in the evolutionary design of digital circuits—Part I. Genet Program Evolvable Mach 1:7–35

    Article  MATH  Google Scholar 

  18. Hartmann M, Haddow PC (2004) Evolution of fault-tolerant and noise robust digital designs. Proc Inst Elect Eng Comp Digit Tech 151(4):287–294

    Article  Google Scholar 

  19. Greenwood GW (2005) On the practicality of using intrinsic reconfiguration for fault recovery. IEEE Trans Evol Comput 9(4):398–405

    Article  Google Scholar 

  20. Santini CC, Amaral JFM, Pacheco MAC, Tanscheit R (2004) Evolvability and reconfigurability. In: Proceedings of IEEE International Conference on Field-Program Technology, pp 105–112

  21. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  22. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  23. Blickle T, Thiele L (1995) A mathematical analysis of tournament selection. In Proceedings of the Sixth International Conference on Genetic Algorithms

  24. Mastorakis NE, Gonos IF, Swamy MS (2003) Design of two-dimensional recursive filters using genetic algorithms. IEEE Trans Circuits Syst I 50(5):634–639

    Article  MathSciNet  Google Scholar 

  25. Thumvichai R, Bose T, Haupt RL (2002) Design of 2-D multiplierless IIR filters using the genetic algorithm. IEEE Trans Circuits Syst I 49(6):878–882

    Article  Google Scholar 

  26. Oner M, Askar M (1999) Incremental design of high complexity FIR filters by genetic algorithms. Proc Int Symp Signal Proces Appl 3:1005–1008

    Google Scholar 

  27. Lee A, Ahmadi M, Jullian GA, Miller WC, Lashkari RS (1999) Design of 1-D FIR filters using genetic algorithms. Proc IEEE Int Symp Circuits Syst 3:295–298

    Google Scholar 

  28. Tang KS, Man KF, Kwong S, Liu ZF (1998) Design and optimization of IIR structure using hierarchical genetic algorithms. IEEE Trans Industrial Electron 45(3):481–487

    Article  Google Scholar 

  29. Kwong S, He QH, Man KF, Tang KS, Chau CW (1998) Parallel genetic-based hybrid pattern matching algorithm for isolated word recognition. Int J Pattern Recog Artif Intel 12(5):573–594

    Article  Google Scholar 

  30. Arslan T, Horrocks DH (1995) A Genetic algorithm for the design of finite word length arbitrary response cascaded IIR digital filters. Proc IEE Conf of Genetic Algo Eng Syst: Innov Appl 414:276–281

    Article  Google Scholar 

  31. Erba M, Rossi R, Liberali V, Tettamanzi AGB (2001) Digital filter design through simulated evolution. Eur Conf Circuit Theory Des 2:137–140

    Google Scholar 

  32. Tufte G, Haddow PC (2000) Evolving an adaptive digital filter. In: Proceedings of the 2000 NASA/DoD Conference on Evolvable Hardware, IEEE Computer Press, pp 143–150

  33. Kalganova T, Miller J (1999) Evolving more efficient digital circuits by allowing circuit layout evolution and multi-objective fitness. In: Proceedings NASA/DoD Workshop Evolvable Hardware, pp 54–63

  34. Miller J (1999) Digital filter design at gate-level using evolutionary algorithms. In: Proceedings of the Genetic Evolutionary Computation Conference (GECCO’99), pp 1127–1134

  35. Uesaka K, Kawamata M (2003) Evolutionary synthesis of digital filter structures using genetic programming. IEEE Trans Circuits Syst II 50:977–983

    Article  Google Scholar 

  36. Uesaka K, Kawamata M (2000) Synthesis of low-sensitivity second-order digital filters using genetic programming with automatically defined functions. IEEE Signal Process Lett 7:83–85

    Article  Google Scholar 

  37. Antoniou A (2005) Digital signal processing: signals, systems, and filters. McGraw-Hill, New York

    Google Scholar 

  38. Agarwal RC, Burrus CS (1975) New recursive digital filter structures having very low sensitivity and roundoff noise. IEEE Trans Circuit Syst 22:921–927

    Article  Google Scholar 

  39. Diniz PSR, Antoniou A (1985) Low-sensitivity digital-filter structures which are amenable to error spectrum shaping. IEEE Trans Circuits Syst 32:1000–1007

    Article  Google Scholar 

  40. Rao YVR, Eswaran C (1989) A pole-sensitivity based method for the design of digital filters for error-spectrum shaping. IEEE Trans Circuits Syst 36:1017–1020

    Article  Google Scholar 

  41. Barnes C (1984) On the design of optimal state-space realizations of second-order digital filters. IEEE Trans Circuits Syst 31:602–608

    Article  MathSciNet  Google Scholar 

  42. Mesquita A, Salazar FA, Canazio PP (2002) Chromosome representation through adjacency matrix in evolutionary circuits synthesis. In: Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware, IEEE Computer Press, Alexandria, pp 102–109

  43. Sapargaliyev Y, Kalganova T (2006) Constrained and unconstrained evolution of LCR low-pass filters with oscillating length representation. IEEE Congress on Evolutionary Computation, pp 1529–1536

  44. Swamy MNS, Thulasiraman K (1981) Graphs, networks and algorithms. Wiley, New York

    MATH  Google Scholar 

  45. Warshall S (1962) A theorem on Boolean matrices. J ACM 9:11–12

    Article  MATH  MathSciNet  Google Scholar 

  46. Zebulum RS, Pacheco MAC, Vellasco MMBR (2001) Evolutionary electronics–automatic design of electronic circuits and systems by genetic algorithms. CRC Press, Boca Raton

    Google Scholar 

  47. Kawamata M, Iwatsuki M, Higuchi T (1985) Balanced realizations as minimum sensitivity structures in linear systems. Trans Soc Instrum Contr Eng 21:900–906

    Google Scholar 

  48. Koza JR et al. (1996) Use of automatically defined functions and architecture-altering operations in automated circuit synthesis with genetic programming. In: Proceedings of the First Annual Conference

  49. Steele GL Jr (1990) Common lisp: the language, 2nd edn. Digital, Bedford

    MATH  Google Scholar 

  50. Lipson H, Schmidt M (2007) Comparison of tree and graph encodings as function of problem complexity. Genetic and evolutionary computation conference (GECCO), England

  51. Di Ferdinando R, Calabretta A, Parisi D (2000) Evolving modular architectures for neural networks. In: Proceedings of the sixth neural computation and psychology workshop: evolution, learning and development

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Authors and Affiliations

  1. Brazilian Army Technological Center, Av das Américas, 28705, Guaratiba, Rio de Janeiro, 23020-470, Brazil

    Leonardo Bruno de Sá

  2. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Antonio Mesquita

Authors
  1. Leonardo Bruno de Sá
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  2. Antonio Mesquita
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Correspondence to Leonardo Bruno de Sá.

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de Sá, L.B., Mesquita, A. Evolutionary synthesis of low-sensitivity digital filters using adjacency matrix. Evol. Intel. 2, 103–120 (2009). https://doi.org/10.1007/s12065-009-0028-x

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  • DOI: https://doi.org/10.1007/s12065-009-0028-x

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