{"title":"神经网络变体的比较分析:实验研究","authors":"S. Vani, T. Madhusudhana Rao, Ch. Kannam Naidu","doi":"10.1109/ICACCS.2019.8728327","DOIUrl":null,"url":null,"abstract":"Neural Networks, with their remarkable capacity to get significance from convoluted information can be utilized to remove patterns that are too composite to be in any way seen by humans. A prepared neural network can be thought of as a specialist in the classification of data which is given to analyze. There are different kinds of Neural Networks like Artificial Neural Network (ANN), Feedforward Neural Network, Recurrent Neural Network(RNN), Recursive Recurrent Neural Network (RRNN), Convolutional Neural Network(CNN), Modular Neural Network (MNN), Restricted Boltzmann Machine (RBM) etc. In this paper, we have discussed the performance of ANN, CNN, RNN, and RBM where CNN has outplayed the remaining with accuracy of 97.81%.","PeriodicalId":249139,"journal":{"name":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Comparative Analysis on variants of Neural Networks: An Experimental Study\",\"authors\":\"S. Vani, T. Madhusudhana Rao, Ch. Kannam Naidu\",\"doi\":\"10.1109/ICACCS.2019.8728327\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Neural Networks, with their remarkable capacity to get significance from convoluted information can be utilized to remove patterns that are too composite to be in any way seen by humans. A prepared neural network can be thought of as a specialist in the classification of data which is given to analyze. There are different kinds of Neural Networks like Artificial Neural Network (ANN), Feedforward Neural Network, Recurrent Neural Network(RNN), Recursive Recurrent Neural Network (RRNN), Convolutional Neural Network(CNN), Modular Neural Network (MNN), Restricted Boltzmann Machine (RBM) etc. In this paper, we have discussed the performance of ANN, CNN, RNN, and RBM where CNN has outplayed the remaining with accuracy of 97.81%.\",\"PeriodicalId\":249139,\"journal\":{\"name\":\"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACCS.2019.8728327\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACCS.2019.8728327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparative Analysis on variants of Neural Networks: An Experimental Study
Neural Networks, with their remarkable capacity to get significance from convoluted information can be utilized to remove patterns that are too composite to be in any way seen by humans. A prepared neural network can be thought of as a specialist in the classification of data which is given to analyze. There are different kinds of Neural Networks like Artificial Neural Network (ANN), Feedforward Neural Network, Recurrent Neural Network(RNN), Recursive Recurrent Neural Network (RRNN), Convolutional Neural Network(CNN), Modular Neural Network (MNN), Restricted Boltzmann Machine (RBM) etc. In this paper, we have discussed the performance of ANN, CNN, RNN, and RBM where CNN has outplayed the remaining with accuracy of 97.81%.