{"title":"Comprehensive evaluation of forest industries based on rough sets and artificial neural network","authors":"Xuechun Yang, Xiangnan Zhang, Qixing Wu","doi":"10.1109/CINC.2010.5643735","DOIUrl":null,"url":null,"abstract":"Scientific and rational assessment of forest industries will help to improve forest management concept and to stabilize supply market of wood products. The forest industries are briefly analyzed to determine 10 evaluation indexes of forestry enterprises. Based on rough sets and BP neural network evaluation model of forest industries, through rough sets theory to evaluating targets for attribute reduction, indicators after the evaluation input to the BP neural network is trained by sample prediction, the prediction results to be in conformity with actual results.","PeriodicalId":227004,"journal":{"name":"2010 Second International Conference on Computational Intelligence and Natural Computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","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.2010.5643735","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Scientific and rational assessment of forest industries will help to improve forest management concept and to stabilize supply market of wood products. The forest industries are briefly analyzed to determine 10 evaluation indexes of forestry enterprises. Based on rough sets and BP neural network evaluation model of forest industries, through rough sets theory to evaluating targets for attribute reduction, indicators after the evaluation input to the BP neural network is trained by sample prediction, the prediction results to be in conformity with actual results.
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