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Citation(s) in
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9
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For:
Li X
, Mao W, Jiang W.
Fast sparse approximation of extreme learning machine.
Neurocomputing
2014. [DOI:
10.1016/j.neucom.2013.01.064
]
[
Citation(s) in
RCA
: 18
]
[
Impact Index Per Article: 1.8
]
[
Reference Citation Analysis
]
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[Indexed: 11/25/2022]
Number
Cited by Other Article(s)
1
Li P
, Zhang QS, Zhang GL, Liu W, Chen FR. Adaptive S transform for feature extraction in voltage sags.
Appl Soft Comput
2019. [DOI:
10.1016/j.asoc.2019.04.021
]
[
Citation(s) in
RCA
: 8
]
[
Impact Index Per Article: 1.6
]
[
Reference Citation Analysis
]
[
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]
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[Indexed: 11/28/2022]
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2
Chen Y
, Kloft M, Yang Y, Li C, Li L. Mixed kernel based extreme learning machine for electric load forecasting.
Neurocomputing
2018. [DOI:
10.1016/j.neucom.2018.05.068
]
[
Citation(s) in
RCA
: 68
]
[
Impact Index Per Article: 11.3
]
[
Reference Citation Analysis
]
[
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]
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[Indexed: 11/29/2022]
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3
Sparse pseudoinverse incremental extreme learning machine.
Neurocomputing
2018. [DOI:
10.1016/j.neucom.2018.01.087
]
[
Citation(s) in
RCA
: 15
]
[
Impact Index Per Article: 2.5
]
[
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[Indexed: 11/21/2022]
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4
Recursive reduced kernel based extreme learning machine for aero-engine fault pattern recognition.
Neurocomputing
2016. [DOI:
10.1016/j.neucom.2016.06.069
]
[
Citation(s) in
RCA
: 24
]
[
Impact Index Per Article: 3.0
]
[
Reference Citation Analysis
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[Indexed: 11/18/2022]
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5
A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis.
ENERGIES
2016. [DOI:
10.3390/en9100828
]
[
Citation(s) in
RCA
: 15
]
[
Impact Index Per Article: 1.9
]
[
Reference Citation Analysis
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[Indexed: 11/16/2022]
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6
Parsimonious kernel extreme learning machine in primal via Cholesky factorization.
Neural Netw
2016;
80
:95-109. [DOI:
10.1016/j.neunet.2016.04.009
]
[
Citation(s) in
RCA
: 18
]
[
Impact Index Per Article: 2.3
]
[
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[Received: 11/25/2015]
[Revised: 04/01/2016]
[Accepted: 04/21/2016]
[Indexed: 11/21/2022]
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7
Zhang S
, Li P, Zhang L, Li H, Jiang W, Hu Y. Modified S transform and ELM algorithms and their applications in power quality analysis.
Neurocomputing
2016. [DOI:
10.1016/j.neucom.2015.12.050
]
[
Citation(s) in
RCA
: 37
]
[
Impact Index Per Article: 4.6
]
[
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[Indexed: 11/26/2022]
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8
Self-controlled bio-inspired extreme learning machines for scalable regression and classification: a comprehensive analysis with some recommendations.
Artif Intell Rev
2016. [DOI:
10.1007/s10462-016-9461-2
]
[
Citation(s) in
RCA
: 3
]
[
Impact Index Per Article: 0.4
]
[
Reference Citation Analysis
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[
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[Indexed: 10/22/2022]
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9
Parsimonious regularized extreme learning machine based on orthogonal transformation.
Neurocomputing
2015. [DOI:
10.1016/j.neucom.2014.12.046
]
[
Citation(s) in
RCA
: 12
]
[
Impact Index Per Article: 1.3
]
[
Reference Citation Analysis
]
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[Indexed: 11/18/2022]
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