基于人工鱼群算法的信号MP稀疏分解

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1、西南交通大学硕士学位论文基于人工鱼群算法的信号MP稀疏分解姓名：舒维杰申请学位级别：硕士专业：信号与信息处理指导教师：尹忠科20080701西南交通大学硕士研究生学位论文第1I页AbstractThesignalsparsedecompositionisemergingitsanewmethodforanalyzingandprocessingsignals，whichhasmanyexcellentcharacteristics．AndsignalsparsedecompositionbasedonMatchingPursu

2、it(MP)isusedcommonlyinsignalsparsedecomposition．Ithasbeenappliedtomanyareassuchasdatacompression，signalfeatureextraction，time—frequencyanalysisandetc．．ButitisaNPdifficultproblem．Thelargecomputationalcostisthebottleneckofsparsedecomposition．Inrecentyears，swarmintelli

3、gencealgorithmhasattractedlotsofresearchesofnumerousscienceareas，whichhashigheradaptability,robustness，parallelandglobalqualityetc．，andareusedwidelyinsomeareassuchasfunctionoptimization，patternrecognitionandimageprocessingetc．Thisarticlestudiestheapplicationofanewsw

4、armintelligencealgorithm—ArtificialFish·SwarmAlgorithm(AFSA)，insolvingsignalsparsedecompositionandtherelatedapplication．Thisevolutionalgorithmcanovercomelocalextremumandgetsomeglobalextremums，initialvaluescallbechosenrandomandcarryingoutthisalgorithmdoesn’tneedtokno

5、wgradientvalueoftheobjectivefunction，SOithasrobustnessforthesearchingarea．ThepaperstartswiththesignalsparsedecompositionandMatchingPursuitalgorithm．AfterwardsArtificialFish-SwarmAlgorithmisintroduced．Itpresentssystemicexpatiationandstudyabouttheprinciple，structure，a

6、stringencyandimplementsmethodsofthealgorithm．Andanimprovedartificialfish-Swarmalgorithmisproposed．Inthepreyactionofartificialfish，thepullmeansisaddedwhichcancollectmoreartificialfishneartheglobalextremum．TheImprovedalgorithmnotonlyretainstheprecisionoforiginalalgori

7、thm，butalsoadvancestheefficiencyandconvergentspeed．ThentheimprovedArtificialFish-SwarmAlgorithmisappliedtothesignalsparsedecompositionbasedonMP,andthesimulationresultsandanalysisaregiven．ItshowsthattheimprovedArtificialFish．SwarmAlgorithmCanfastsearchforapproximatel

8、yoptimalatomateachstepofMPandthequantumofcomputingisreducedalot．Inaddition，itisinevitableto西南交通大学硕士研究生学位论文第1II页●

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