SMV methodology enhancements for high speed I/O links of SoCs
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Year
: 2014DOI: 10.1109/AUPEC.2014.6966627
Keyword(s): Artificial neural networks,Forecasting,Genetic algorithms,Load forecasting,Load modeling,Predictive models,Training,Feed-forward neural network,genetic algorithm,power System Planning,radial basis function neural network,short-term load forecasting
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SMV methodology enhancements for high speed I/O links of SoCs
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| date accessioned | 2020-03-12T19:46:37Z | |
| date available | 2020-03-12T19:46:37Z | |
| date issued | 2014 | |
| identifier other | 6818767.pdf | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/989698 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | SMV methodology enhancements for high speed I/O links of SoCs | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8105960 | |
| subject keywords | Artificial neural networks | |
| subject keywords | Forecasting | |
| subject keywords | Genetic algorithms | |
| subject keywords | Load forecasting | |
| subject keywords | Load modeling | |
| subject keywords | Predictive models | |
| subject keywords | Training | |
| subject keywords | Feed-forward neural network | |
| subject keywords | genetic algorithm | |
| subject keywords | power System Planning | |
| subject keywords | radial basis function neural network | |
| subject keywords | short-term load forecasting | |
| identifier doi | 10.1109/AUPEC.2014.6966627 | |
| journal title | LSI Test Symposium (VTS), 2014 IEEE 32nd | |
| filesize | 539848 | |
| citations | 0 | |
| contributor rawauthor | Viveros-Wacher, A. , Alejos, R. , Alvarez, L. , Diaz-Castro, I. , Marcial, B. , Motola-Acuna, G. , Vega-Ochoa, E.-A. |