Genetic algorithm optimization for coefficient of FFT processor
This paper describes the implementation of Single-objective Genetic Algorithm (SOGA) and Multi-objectives Genetic Algorithm (MOGA) to optimize the pipelined Fast Fourier Transform (FFT) coefficient in order to improve the performance of Signal to Noise Ratio (SNR) and also the Switching Activity (SA...
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American-Eurasian Network for Scientific Information
2010
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my.upm.eprints.148722019-05-08T07:27:19Z http://psasir.upm.edu.my/id/eprint/14872/ Genetic algorithm optimization for coefficient of FFT processor Pang, Jia Hong Sulaiman, Nasri This paper describes the implementation of Single-objective Genetic Algorithm (SOGA) and Multi-objectives Genetic Algorithm (MOGA) to optimize the pipelined Fast Fourier Transform (FFT) coefficient in order to improve the performance of Signal to Noise Ratio (SNR) and also the Switching Activity (SA). The SA and SNR are optimized separately in a Radix-4 Single Path Delay Feedback (R4SDF) pipelined Fast Fourier Transform (FFT) processor using SOGA. The MOGA optimized both objectives using Weighted-Sum approach. American-Eurasian Network for Scientific Information 2010 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/14872/1/Genetic%20algorithm%20optimization%20for%20coefficient%20of%20FFT%20processor.pdf Pang, Jia Hong and Sulaiman, Nasri (2010) Genetic algorithm optimization for coefficient of FFT processor. Australian Journal of Basic and Applied Sciences, 4 (9). pp. 4184-4192. ISSN 1991-8178 http://www.ajbasweb.com/old/Ajbas_september_2010.html |
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English |
| description |
This paper describes the implementation of Single-objective Genetic Algorithm (SOGA) and Multi-objectives Genetic Algorithm (MOGA) to optimize the pipelined Fast Fourier Transform (FFT) coefficient in order to improve the performance of Signal to Noise Ratio (SNR) and also the Switching Activity (SA). The SA and SNR are optimized separately in a Radix-4 Single Path Delay Feedback (R4SDF) pipelined Fast Fourier Transform (FFT) processor using SOGA. The MOGA optimized both objectives using Weighted-Sum approach. |
| format |
Article |
| author |
Pang, Jia Hong Sulaiman, Nasri |
| spellingShingle |
Pang, Jia Hong Sulaiman, Nasri Genetic algorithm optimization for coefficient of FFT processor |
| author_facet |
Pang, Jia Hong Sulaiman, Nasri |
| author_sort |
Pang, Jia Hong |
| title |
Genetic algorithm optimization for coefficient of FFT processor |
| title_short |
Genetic algorithm optimization for coefficient of FFT processor |
| title_full |
Genetic algorithm optimization for coefficient of FFT processor |
| title_fullStr |
Genetic algorithm optimization for coefficient of FFT processor |
| title_full_unstemmed |
Genetic algorithm optimization for coefficient of FFT processor |
| title_sort |
genetic algorithm optimization for coefficient of fft processor |
| publisher |
American-Eurasian Network for Scientific Information |
| publishDate |
2010 |
| url |
http://psasir.upm.edu.my/id/eprint/14872/1/Genetic%20algorithm%20optimization%20for%20coefficient%20of%20FFT%20processor.pdf http://psasir.upm.edu.my/id/eprint/14872/ http://www.ajbasweb.com/old/Ajbas_september_2010.html |
| _version_ |
1643825764763697152 |
| score |
13.250461 |