FEM analysis of dielectric loaded waveguides with additive hierarchical singular vector elements
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: 2014DOI: 10.1109/PESTSE.2014.6805251
Keyword(s): learning (artificial intelligence),n neural nets,n optimisation,n power engineering computing,n power markets,n pricing,n profitability,n tendering,n ABC,n CS algorithm,n Cuckoo search algorithm,n NN,n adaptive technique based modeling,n artificial bees colony,n competitive electricity market,n employee bee,n learning,n minimum pricing level,n neural network,n onlooker bee,n optimal bidding strategies,n optimization tool,n profit
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FEM analysis of dielectric loaded waveguides with additive hierarchical singular vector elements
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| contributor author | Graglia, Roberto D. | |
| contributor author | Peterson, Andrew F. | |
| contributor author | Matekovits, Ladislau | |
| contributor author | Petrini, Paolo | |
| date accessioned | 2020-03-12T21:43:20Z | |
| date available | 2020-03-12T21:43:20Z | |
| date issued | 2014 | |
| identifier other | 6955487.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1058128 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | FEM analysis of dielectric loaded waveguides with additive hierarchical singular vector elements | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8189465 | |
| subject keywords | learning (artificial intelligence) | |
| subject keywords | n neural nets | |
| subject keywords | n optimisation | |
| subject keywords | n power engineering computing | |
| subject keywords | n power markets | |
| subject keywords | n pricing | |
| subject keywords | n profitability | |
| subject keywords | n tendering | |
| subject keywords | n ABC | |
| subject keywords | n CS algorithm | |
| subject keywords | n Cuckoo search algorithm | |
| subject keywords | n NN | |
| subject keywords | n adaptive technique based modeling | |
| subject keywords | n artificial bees colony | |
| subject keywords | n competitive electricity market | |
| subject keywords | n employee bee | |
| subject keywords | n learning | |
| subject keywords | n minimum pricing level | |
| subject keywords | n neural network | |
| subject keywords | n onlooker bee | |
| subject keywords | n optimal bidding strategies | |
| subject keywords | n optimization tool | |
| subject keywords | n profit | |
| identifier doi | 10.1109/PESTSE.2014.6805251 | |
| journal title | adio Science Meeting (Joint with AP-S Symposium), 2014 USNC-URSI | |
| filesize | 43077 | |
| citations | 0 |