Hocine Merabti
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.
- 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.Google Scholar
Cross Ref
- V. Duong and A. R. Stubberud, "System identification by genetic algorithm," IEEE Aerospace Conference Proceedings, 2002, pp. 5--2331--5--2337 vol.5.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google Scholar
Index Terms
- FPGA based implementation of a genetic algorithm for ARMA model parameters identification
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