I. Pavaloiu, A. Vasile, Sebastian Marius Rosu, G. Dragoi
{"title":"前馈多层相位神经网络","authors":"I. Pavaloiu, A. Vasile, Sebastian Marius Rosu, G. Dragoi","doi":"10.1109/NEUREL.2014.7011478","DOIUrl":null,"url":null,"abstract":"Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are simple CVNNs that use for the internal weights complex numbers with the modulus 1, the only adaptable parameters being the phases of the weights. We present in this paper some limitations of the Continuous Phase-Based Neuron (CPBN) and describe the structure of a Feedforward Multilayer Phase-Based Neural Network (MLPBN) and its training using an adaptation of the backpropagation algorithm.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Feedforward multilayer phase-based neural networks\",\"authors\":\"I. Pavaloiu, A. Vasile, Sebastian Marius Rosu, G. Dragoi\",\"doi\":\"10.1109/NEUREL.2014.7011478\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are simple CVNNs that use for the internal weights complex numbers with the modulus 1, the only adaptable parameters being the phases of the weights. We present in this paper some limitations of the Continuous Phase-Based Neuron (CPBN) and describe the structure of a Feedforward Multilayer Phase-Based Neural Network (MLPBN) and its training using an adaptation of the backpropagation algorithm.\",\"PeriodicalId\":402208,\"journal\":{\"name\":\"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"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.2014.7011478\",\"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.2014.7011478","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are simple CVNNs that use for the internal weights complex numbers with the modulus 1, the only adaptable parameters being the phases of the weights. We present in this paper some limitations of the Continuous Phase-Based Neuron (CPBN) and describe the structure of a Feedforward Multilayer Phase-Based Neural Network (MLPBN) and its training using an adaptation of the backpropagation algorithm.