{"title":"时间序列预测前馈网络协同进化的改进神经元-突触级问题分解方法","authors":"Ravneil Nand, B. Sharma","doi":"10.1109/APWCONCSE.2017.00039","DOIUrl":null,"url":null,"abstract":"Complex problems have been solved efficiently through decomposition of a particular problem using problem decompositions. Even combination of different distinct problem decomposition methods has shown good results in time series prediction. The application of different problem decomposition methods at different stages of a network can share its strengths to solve the problem in hand better. Hybrid versions of two distinct problem decomposition methods has showed promising results in past. In this paper, a modified version of latterly introduced Neuron-Synapse level problem decomposition is proposed using feedforward neural networks for time series prediction. The results shows that the proposed modified model has got better results in more datasets when compared to its previous version. The results are better in some cases for proposed method in comparison to several other methods from the literature.","PeriodicalId":215519,"journal":{"name":"2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Modified Neuron-Synapse Level Problem Decomposition Method for Cooperative Coevolution of Feedforward Networks for Time Series Prediction\",\"authors\":\"Ravneil Nand, B. Sharma\",\"doi\":\"10.1109/APWCONCSE.2017.00039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Complex problems have been solved efficiently through decomposition of a particular problem using problem decompositions. Even combination of different distinct problem decomposition methods has shown good results in time series prediction. The application of different problem decomposition methods at different stages of a network can share its strengths to solve the problem in hand better. Hybrid versions of two distinct problem decomposition methods has showed promising results in past. In this paper, a modified version of latterly introduced Neuron-Synapse level problem decomposition is proposed using feedforward neural networks for time series prediction. The results shows that the proposed modified model has got better results in more datasets when compared to its previous version. The results are better in some cases for proposed method in comparison to several other methods from the literature.\",\"PeriodicalId\":215519,\"journal\":{\"name\":\"2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APWCONCSE.2017.00039\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APWCONCSE.2017.00039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modified Neuron-Synapse Level Problem Decomposition Method for Cooperative Coevolution of Feedforward Networks for Time Series Prediction
Complex problems have been solved efficiently through decomposition of a particular problem using problem decompositions. Even combination of different distinct problem decomposition methods has shown good results in time series prediction. The application of different problem decomposition methods at different stages of a network can share its strengths to solve the problem in hand better. Hybrid versions of two distinct problem decomposition methods has showed promising results in past. In this paper, a modified version of latterly introduced Neuron-Synapse level problem decomposition is proposed using feedforward neural networks for time series prediction. The results shows that the proposed modified model has got better results in more datasets when compared to its previous version. The results are better in some cases for proposed method in comparison to several other methods from the literature.