{"title":"为典型工业过程的AIMNC战略提供适当的训练集","authors":"Jasmin Igic, M. Bozic","doi":"10.1109/NEUREL.2014.7011502","DOIUrl":null,"url":null,"abstract":"Here we have discussed how the training data set should be selected for the Approximate Internal Model-based Neural Control (AIMNC) applied to the typical industrial processes. In the considered control strategy only one neural network (NN), Multi Layer NN (MLNN), which is the neural model of the plant, should be trained off-line. An inverse neural controller can be directly obtained from the neural model without necessity of a further training. Simulations demonstrate performance of the AIMNC strategy for NN model obtained with adequate training set.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An adequate training set for the AIMNC strategy for typical industrial processes\",\"authors\":\"Jasmin Igic, M. Bozic\",\"doi\":\"10.1109/NEUREL.2014.7011502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Here we have discussed how the training data set should be selected for the Approximate Internal Model-based Neural Control (AIMNC) applied to the typical industrial processes. In the considered control strategy only one neural network (NN), Multi Layer NN (MLNN), which is the neural model of the plant, should be trained off-line. An inverse neural controller can be directly obtained from the neural model without necessity of a further training. Simulations demonstrate performance of the AIMNC strategy for NN model obtained with adequate training set.\",\"PeriodicalId\":402208,\"journal\":{\"name\":\"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2014.7011502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2014.7011502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An adequate training set for the AIMNC strategy for typical industrial processes
Here we have discussed how the training data set should be selected for the Approximate Internal Model-based Neural Control (AIMNC) applied to the typical industrial processes. In the considered control strategy only one neural network (NN), Multi Layer NN (MLNN), which is the neural model of the plant, should be trained off-line. An inverse neural controller can be directly obtained from the neural model without necessity of a further training. Simulations demonstrate performance of the AIMNC strategy for NN model obtained with adequate training set.