{"title":"基于遗传算法的人工神经网络结构进化设计","authors":"N. Moharamzade, F. Farokhi","doi":"10.1109/CINC.2010.5643748","DOIUrl":null,"url":null,"abstract":"Determining the optimum structure for an Artificial Network is an important design step in almost all the artificial intelligence systems which are based on Neural or neuro-fuzzy networks. In this paper a genetic algorithm based solution is presented and tested over real world databases and for single layer and multiple layer networks and it has been shown that the determined network structures has the best accuracy and the optimized topology as well.","PeriodicalId":227004,"journal":{"name":"2010 Second International Conference on Computational Intelligence and Natural Computing","volume":"8 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Evolutionary design of ANN structure using genetic algorithm\",\"authors\":\"N. Moharamzade, F. Farokhi\",\"doi\":\"10.1109/CINC.2010.5643748\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Determining the optimum structure for an Artificial Network is an important design step in almost all the artificial intelligence systems which are based on Neural or neuro-fuzzy networks. In this paper a genetic algorithm based solution is presented and tested over real world databases and for single layer and multiple layer networks and it has been shown that the determined network structures has the best accuracy and the optimized topology as well.\",\"PeriodicalId\":227004,\"journal\":{\"name\":\"2010 Second International Conference on Computational Intelligence and Natural Computing\",\"volume\":\"8 3\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Second International Conference on Computational Intelligence and Natural Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CINC.2010.5643748\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Computational Intelligence and Natural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINC.2010.5643748","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evolutionary design of ANN structure using genetic algorithm
Determining the optimum structure for an Artificial Network is an important design step in almost all the artificial intelligence systems which are based on Neural or neuro-fuzzy networks. In this paper a genetic algorithm based solution is presented and tested over real world databases and for single layer and multiple layer networks and it has been shown that the determined network structures has the best accuracy and the optimized topology as well.