Farah Ayiesya Zainuddin, Md Fahmi Abd Samad, Hishamuddin Jamaluddin, Abul K. M. Azad
{"title":"离散时间系统识别遗传算法中的新型单亲交配技术","authors":"Farah Ayiesya Zainuddin, Md Fahmi Abd Samad, Hishamuddin Jamaluddin, Abul K. M. Azad","doi":"10.37934/araset.42.2.4957","DOIUrl":null,"url":null,"abstract":"System identification is concerned with the construction of a mathematical model based on given input and output data to represent the dynamical behaviour of a system. As a step-in system identification, model structure selection is a step where a model perceived as adequate system representation is selected. A typical rule is that the model must have a good balance between parsimony and accuracy in estimating a dynamic system. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the optimality of GA. This paper introduces a novel mating technique in GA based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that demonstrates high exploration capability. In investigating this, four systems of linear and nonlinear classes were simulated to generate discrete-time sets of data i.e. later used for identification. The outcome shows that GA incorporated with the mating technique within 10% - 20% of the population size is able to find optimal models quicker than the traditional GA.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Single Parent Mating Technique in Genetic Algorithm for Discrete – Time System Identification\",\"authors\":\"Farah Ayiesya Zainuddin, Md Fahmi Abd Samad, Hishamuddin Jamaluddin, Abul K. M. Azad\",\"doi\":\"10.37934/araset.42.2.4957\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"System identification is concerned with the construction of a mathematical model based on given input and output data to represent the dynamical behaviour of a system. As a step-in system identification, model structure selection is a step where a model perceived as adequate system representation is selected. A typical rule is that the model must have a good balance between parsimony and accuracy in estimating a dynamic system. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the optimality of GA. This paper introduces a novel mating technique in GA based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that demonstrates high exploration capability. In investigating this, four systems of linear and nonlinear classes were simulated to generate discrete-time sets of data i.e. later used for identification. The outcome shows that GA incorporated with the mating technique within 10% - 20% of the population size is able to find optimal models quicker than the traditional GA.\",\"PeriodicalId\":506443,\"journal\":{\"name\":\"Journal of Advanced Research in Applied Sciences and Engineering Technology\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advanced Research in Applied Sciences and Engineering Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37934/araset.42.2.4957\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Research in Applied Sciences and Engineering Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37934/araset.42.2.4957","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Single Parent Mating Technique in Genetic Algorithm for Discrete – Time System Identification
System identification is concerned with the construction of a mathematical model based on given input and output data to represent the dynamical behaviour of a system. As a step-in system identification, model structure selection is a step where a model perceived as adequate system representation is selected. A typical rule is that the model must have a good balance between parsimony and accuracy in estimating a dynamic system. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the optimality of GA. This paper introduces a novel mating technique in GA based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that demonstrates high exploration capability. In investigating this, four systems of linear and nonlinear classes were simulated to generate discrete-time sets of data i.e. later used for identification. The outcome shows that GA incorporated with the mating technique within 10% - 20% of the population size is able to find optimal models quicker than the traditional GA.