{"title":"从非线性回归问题的人工神经网络中提取多元线性回归方程的算法的开发与应用","authors":"Veronica Chan, Christine W. Chan","doi":"10.1109/ICCI-CC.2016.7862080","DOIUrl":null,"url":null,"abstract":"This paper discusses the development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems, the algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. Rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The developed algorithm was applied to nineteen datasets. The preliminary results showed that the algorithm gives satisfactory results on sixteen of the nineteen tested datasets and the results demonstrate high fidelity to the originally trained neural network models. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that there are more factors affecting the fidelity of the algorithm apart from the precision of the approximation of each individual node of the given ANN model. Nevertheless, the algorithm shows promising potential for application in engineering problems.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Development and application of an algorithm for extracting multiple linear regression equations from artificial neural networks for nonlinear regression problems\",\"authors\":\"Veronica Chan, Christine W. Chan\",\"doi\":\"10.1109/ICCI-CC.2016.7862080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses the development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems, the algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. Rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The developed algorithm was applied to nineteen datasets. The preliminary results showed that the algorithm gives satisfactory results on sixteen of the nineteen tested datasets and the results demonstrate high fidelity to the originally trained neural network models. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that there are more factors affecting the fidelity of the algorithm apart from the precision of the approximation of each individual node of the given ANN model. Nevertheless, the algorithm shows promising potential for application in engineering problems.\",\"PeriodicalId\":135701,\"journal\":{\"name\":\"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCI-CC.2016.7862080\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCI-CC.2016.7862080","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development and application of an algorithm for extracting multiple linear regression equations from artificial neural networks for nonlinear regression problems
This paper discusses the development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems, the algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. Rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The developed algorithm was applied to nineteen datasets. The preliminary results showed that the algorithm gives satisfactory results on sixteen of the nineteen tested datasets and the results demonstrate high fidelity to the originally trained neural network models. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that there are more factors affecting the fidelity of the algorithm apart from the precision of the approximation of each individual node of the given ANN model. Nevertheless, the algorithm shows promising potential for application in engineering problems.