2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) 2017
DOI: 10.1109/itcosp.2017.8303158
View full text |Buy / Rent full text
|
Sign up to set email alerts
|
Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2020
2020
2021
2021

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 22 publications
0
2
0
Order By: Relevance
“…In the literature, ensembles of ELMs are used mostly for stability classification [19], [29], [32], [35], [56], [75], [76], or combined with random vector functional links [31], [33], [77]. It was also used for regression, to predict load stability margins [78], the fault-induced voltage recovery [30], [79], and maximum frequency deviation and time [80].…”
Section: Learning a Modelmentioning
confidence: 99%
“…In the literature, ensembles of ELMs are used mostly for stability classification [19], [29], [32], [35], [56], [75], [76], or combined with random vector functional links [31], [33], [77]. It was also used for regression, to predict load stability margins [78], the fault-induced voltage recovery [30], [79], and maximum frequency deviation and time [80].…”
Section: Learning a Modelmentioning
confidence: 99%
“…MLPNN is a supervised learning algorithm neural network method that has proven to be a strong algorithm for modelling classification problems (Meng et al 2013). A major disadvantage of MLPNN is that adding the number of hidden neurons does not necessarily improve its prediction accuracy and can increase the computational time (Duraipandy and Devaraj 2018). This feature reduces the performance of MLPNN, especially when dealing with large datasets containing large numbers of predictors with many interrelationships.…”
Section: Introductionmentioning
confidence: 99%