{"title":"应用Hopfield网络求布尔函数的最小代价覆盖","authors":"P. Chu","doi":"10.1109/GLSV.1991.143963","DOIUrl":null,"url":null,"abstract":"To find a minimal expression of a Boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. In this paper, the author tries to apply neural network approach to solve this problem. He first formulates this problem and then defines an 'energy function' and maps it to a modified Hopfield network, which will automatically search for minima.<<ETX>>","PeriodicalId":261873,"journal":{"name":"[1991] Proceedings. First Great Lakes Symposium on VLSI","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Applying Hopfield network to find the minimum cost coverage of a Boolean function\",\"authors\":\"P. Chu\",\"doi\":\"10.1109/GLSV.1991.143963\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To find a minimal expression of a Boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. In this paper, the author tries to apply neural network approach to solve this problem. He first formulates this problem and then defines an 'energy function' and maps it to a modified Hopfield network, which will automatically search for minima.<<ETX>>\",\"PeriodicalId\":261873,\"journal\":{\"name\":\"[1991] Proceedings. First Great Lakes Symposium on VLSI\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1991] Proceedings. First Great Lakes Symposium on VLSI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GLSV.1991.143963\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1991] Proceedings. First Great Lakes Symposium on VLSI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GLSV.1991.143963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Applying Hopfield network to find the minimum cost coverage of a Boolean function
To find a minimal expression of a Boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. In this paper, the author tries to apply neural network approach to solve this problem. He first formulates this problem and then defines an 'energy function' and maps it to a modified Hopfield network, which will automatically search for minima.<>