A new stability condition for discrete time recurrent neural networks with complex-valued linear threshold neurons
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: 2014DOI: 10.1109/EMBC.2014.6944185
Keyword(s): data mining,drug delivery systems,electronic health records,drug interaction signal detection,drug safety professionals,epidemiology study,functional temporal interest,patient electronic health data,post-marketing surveillance,temporal association mining techniques,temporal interestingness measure,Association rules,Drugs,Electric potential,Hospitals,Surveillance
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A new stability condition for discrete time recurrent neural networks with complex-valued linear threshold neurons
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| contributor author | Wei Zhou , Zurada, J.M. | |
| date accessioned | 2020-03-12T23:24:04Z | |
| date available | 2020-03-12T23:24:04Z | |
| date issued | 2014 | |
| identifier other | 6889621.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1110161 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | A new stability condition for discrete time recurrent neural networks with complex-valued linear threshold neurons | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8278841 | |
| subject keywords | data mining | |
| subject keywords | drug delivery systems | |
| subject keywords | electronic health records | |
| subject keywords | drug interaction signal detection | |
| subject keywords | drug safety professionals | |
| subject keywords | epidemiology study | |
| subject keywords | functional temporal interest | |
| subject keywords | patient electronic health data | |
| subject keywords | post-marketing surveillance | |
| subject keywords | temporal association mining techniques | |
| subject keywords | temporal interestingness measure | |
| subject keywords | Association rules | |
| subject keywords | Drugs | |
| subject keywords | Electric potential | |
| subject keywords | Hospitals | |
| subject keywords | Surveillance | |
| identifier doi | 10.1109/EMBC.2014.6944185 | |
| journal title | eural Networks (IJCNN), 2014 International Joint Conference on | |
| filesize | 703685 | |
| citations | 0 |