Multi-modulus algorithm based on global artificial fish swarm intelligent optimization of DNA encoding sequences
Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding seq...
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Veröffentlicht in: | Genetics and molecular research 2015-12, Vol.14 (4), p.17511-17518 |
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creator | Guo, Y C Wang, H Wu, H P Zhang, M Q |
description | Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA. |
doi_str_mv | 10.4238/2015.December.21.23 |
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To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. 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To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.</description><subject>Algorithms</subject><subject>DNA - genetics</subject><subject>Models, Theoretical</subject><issn>1676-5680</issn><issn>1676-5680</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkUtPwzAQhC0EglL4BUjIRy4pfsSOc6xaXlKBC5wtx9kUoyQutiMEv54gWsSN085KMzsrfQidUTLLGVeXjFAxW4KFroIwY3TG-B6aUFnITEhF9v_oI3Qc4yshTOSKHKIjJgvFeCkmaHM_tMllna-HdojYtGsfXHrpcGUi1Nj3eN36yrTYhOQaZ90oGxdfcHw3ocOuT9C2bg19wn6TXOc-TXJjyjd4-TDH0Ftfu36NI7wN4wLxBB00po1wup1T9Hx99bS4zVaPN3eL-SqznPOUGWOsInleyKK0FGphQMraClURYkCQsuG5aKDmRlTALWFWFXUujbCq4UIBn6KLn7ub4MfqmHTnoh2fNT34IWqqWFHSUhTqf2shiSqF4my08h-rDT7GAI3eBNeZ8KEp0d9U9DcVvaOiGdWMj6nzbcFQdVD_ZnYY-BcXkIyy</recordid><startdate>20151221</startdate><enddate>20151221</enddate><creator>Guo, Y C</creator><creator>Wang, H</creator><creator>Wu, H P</creator><creator>Zhang, M Q</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope></search><sort><creationdate>20151221</creationdate><title>Multi-modulus algorithm based on global artificial fish swarm intelligent optimization of DNA encoding sequences</title><author>Guo, Y C ; Wang, H ; Wu, H P ; Zhang, M Q</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-aaac80447679c1ed5ae66dc58b00ae509f345fed3a5be3c02c87d46a5c8f358e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>DNA - genetics</topic><topic>Models, Theoretical</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guo, Y C</creatorcontrib><creatorcontrib>Wang, H</creatorcontrib><creatorcontrib>Wu, H P</creatorcontrib><creatorcontrib>Zhang, M Q</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><jtitle>Genetics and molecular research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guo, Y C</au><au>Wang, H</au><au>Wu, H P</au><au>Zhang, M Q</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-modulus algorithm based on global artificial fish swarm intelligent optimization of DNA encoding sequences</atitle><jtitle>Genetics and molecular research</jtitle><addtitle>Genet Mol Res</addtitle><date>2015-12-21</date><risdate>2015</risdate><volume>14</volume><issue>4</issue><spage>17511</spage><epage>17518</epage><pages>17511-17518</pages><issn>1676-5680</issn><eissn>1676-5680</eissn><abstract>Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.</abstract><cop>Brazil</cop><pmid>26782395</pmid><doi>10.4238/2015.December.21.23</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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title | Multi-modulus algorithm based on global artificial fish swarm intelligent optimization of DNA encoding sequences |
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