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FPGA based implementation of a genetic algorithm for ARMA model parameters identification

Authors:

Hocine Merabti,

Daniel MassicotteAuthors Info & Claims

GLSVLSI '14: Proceedings of the 24th edition of the great lakes symposium on VLSI

Pages 95 - 96

https://doi.org/10.1145/2591513.2591579

Published: 20 May 2014 Publication History

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Abstract

In this paper, we propose an FPGA implementation of a genetic algorithm (GA) for linear and nonlinear auto regressive moving average (ARMA) model parameters identification. The GA features specifically designed genetic operators for adaptive filtering applications. The design was implemented using very low bit-wordlength fixed-point representation, where only 6-bit wordlength arithmetic was used. The implementation experiments show high parameters identification capabilities and low footprint.

References

[1]

T. Cassar, K. P. Camilleri, and S. G. Fabri, "Order Estimation of Multivariate ARMA Models," IEEE Journal of Selected Topics in Signal Processing, vol. 4, pp. 494--503, 2010.

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[2]

V. Duong and A. R. Stubberud, "System identification by genetic algorithm," IEEE Aerospace Conference Proceedings, 2002, pp. 5--2331--5--2337 vol.5.

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[3]

Cheng-Yuan, C. and C. Deng-Rui, "Active Noise Cancellation Without Secondary Path Identification by Using an Adaptive Genetic Algorithm," IEEE Transactions on Instrumentation and Measurement, 59(9), 2010, pp. 2315--2327.

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[4]

D. Massicotte and D. Eke, "High robustness to quantification effect of an adaptive filter based on genetic algorithm," IEEE Northeast Workshop on Circuits and Systems (NEWCAS), 2007, pp. 373--376.

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[5]

H. Merabti and D. Massicotte, "Towards Hardware Implementation of Genetic Algorithms for Adaptive Filtering Applications," IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2014, to appear.

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Index Terms

FPGA based implementation of a genetic algorithm for ARMA model parameters identification
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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Published In

GLSVLSI '14: Proceedings of the 24th edition of the great lakes symposium on VLSI

May 2014

376 pages

ISBN:9781450328166

DOI:10.1145/2591513

General Chairs:
Joseph R. Cavallaro
Rice University, USA
,
Tong Zhang
Rensselaer Polytechnic Inst., USA
,
Program Chairs:
Alex K. Jones
University of Pittsburgh, USA
,
Hai (Helen) Li
University of Pittsburgh, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 20 May 2014

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SIGDA

GLSVLSI '14: Great Lakes Symposium on VLSI 2014

May 21 - 23, 2014

Texas, Houston, USA

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GLSVLSI '14 Paper Acceptance Rate 49 of 179 submissions, 27%;

Overall Acceptance Rate 312 of 1,156 submissions, 27%

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GLSVLSI '25

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Great Lakes Symposium on VLSI 2025

June 30 - July 2, 2025

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Model identification of ARIMA family using genetic algorithms

High-Performance Parallel Implementation of Genetic Algorithm on FPGA

A High-Performance, Pipelined, FPGA-Based Genetic Algorithm Machine

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