Partial adversarial training for prediction interval
Version 2 2024-06-04, 06:40Version 2 2024-06-04, 06:40
Version 1 2019-02-12, 11:52Version 1 2019-02-12, 11:52
conference contribution
posted on 2024-06-04, 06:40 authored by HMD Kabir, Abbas KhosraviAbbas Khosravi, Anwar HosenAnwar Hosen, S Nahavandi© 2018 IEEE. Neural network (NN) based prediction or detection systems often perform excellently with easy problems without considering 1-5% difficult problems. This work proposes an adversarial NN training method for constructing the prediction interval (PI). The proposed training method considers adverse situations where traditional NN based PIs frequently fail. First, the conventional lower upper bound estimation (LUBE) method is applied in parallel for initial training of NNs with different initialization. Each NN based PI fails to cover a few samples. Input combinations of those samples are adversely changed by a small amount to generate the adverse samples. A new dataset is generated by appending adverse samples. Finally, an NN is trained with the adverse dataset. The method is applied to construct the NN for wind power prediction. According to the result analysis, the proposed method performs better in adverse situations.
History
Volume
2018-JulyPagination
1-6Location
Rio, BrasilPublisher DOI
Start date
2018-07-08End date
2018-07-13ISBN-13
9781509060146Language
engPublication classification
E Conference publication, E1 Full written paper - refereedTitle of proceedings
IJCNN 2018 : Proceedings of the International Joint Conference on Neural NetworksEvent
Neural Networks. Conference (2018 : Rio, Brasil)Publisher
IEEEPlace of publication
Piscataway, N.J.Usage metrics
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