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Universidade Nova

1. Oliveira, Tiago Miguel Brites. Recursive neuro fuzzy techniques for online identification and control.

Degree: 2013, Universidade Nova

URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/10552

►

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

The main goal of this thesis will be focused on developing an… (more)

Subjects/Keywords: Recursive optimization; Online identification; Adaptative control; Self learning; Kalman filtering; Unscented transformation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Oliveira, T. M. B. (2013). Recursive neuro fuzzy techniques for online identification and control. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/10552

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Oliveira, Tiago Miguel Brites. “Recursive neuro fuzzy techniques for online identification and control.” 2013. Thesis, Universidade Nova. Accessed April 18, 2021. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/10552.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Oliveira, Tiago Miguel Brites. “Recursive neuro fuzzy techniques for online identification and control.” 2013. Web. 18 Apr 2021.

Vancouver:

Oliveira TMB. Recursive neuro fuzzy techniques for online identification and control. [Internet] [Thesis]. Universidade Nova; 2013. [cited 2021 Apr 18]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/10552.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Oliveira TMB. Recursive neuro fuzzy techniques for online identification and control. [Thesis]. Universidade Nova; 2013. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/10552

Not specified: Masters Thesis or Doctoral Dissertation

2.
Qiao, Chenxi.
*Unscented**Transformation*-based Probabilistic Optimal Power Flow.

Degree: 2015, University of Nevada – Reno

URL: http://hdl.handle.net/11714/2679

► Renewable energy-based generation causes uncertainties in power system operation and planning due to its stochastic nature. The load uncertainties combined with the increasing penetration of…
(more)

Subjects/Keywords: locational marginal price; probabilistic optimal power flow; probabilistic power flow; unscented transformation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Qiao, C. (2015). Unscented Transformation-based Probabilistic Optimal Power Flow. (Thesis). University of Nevada – Reno. Retrieved from http://hdl.handle.net/11714/2679

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Qiao, Chenxi. “Unscented Transformation-based Probabilistic Optimal Power Flow.” 2015. Thesis, University of Nevada – Reno. Accessed April 18, 2021. http://hdl.handle.net/11714/2679.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Qiao, Chenxi. “Unscented Transformation-based Probabilistic Optimal Power Flow.” 2015. Web. 18 Apr 2021.

Vancouver:

Qiao C. Unscented Transformation-based Probabilistic Optimal Power Flow. [Internet] [Thesis]. University of Nevada – Reno; 2015. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/11714/2679.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Qiao C. Unscented Transformation-based Probabilistic Optimal Power Flow. [Thesis]. University of Nevada – Reno; 2015. Available from: http://hdl.handle.net/11714/2679

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

3. Hall, Zachary Joseph. Higher Order Polynomial Approximation For Applications in Space Situational Awareness.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/16061zyh5059

► The uncertain Lambert problem and computation of reachability sets are in many ways complimentary problems with important applications in Space Situational Awareness (SSA). Formulating the…
(more)

Subjects/Keywords: space situational awareness; conjugate unscented transformation; polynomial approximation; higher order sensitivity; uncertain lambert problem; reachability sets

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Hall, Z. J. (2018). Higher Order Polynomial Approximation For Applications in Space Situational Awareness. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16061zyh5059

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Hall, Zachary Joseph. “Higher Order Polynomial Approximation For Applications in Space Situational Awareness.” 2018. Thesis, Penn State University. Accessed April 18, 2021. https://submit-etda.libraries.psu.edu/catalog/16061zyh5059.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hall, Zachary Joseph. “Higher Order Polynomial Approximation For Applications in Space Situational Awareness.” 2018. Web. 18 Apr 2021.

Vancouver:

Hall ZJ. Higher Order Polynomial Approximation For Applications in Space Situational Awareness. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Apr 18]. Available from: https://submit-etda.libraries.psu.edu/catalog/16061zyh5059.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hall ZJ. Higher Order Polynomial Approximation For Applications in Space Situational Awareness. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/16061zyh5059

Not specified: Masters Thesis or Doctoral Dissertation

University of New Orleans

4.
Li, Yi.
Application of Monte Carlo (MC) method and *Unscented* *Transformation* (UT) method into Trajectory Prediction of a Dropped Cylinder in Two Dimensions.

Degree: MS, Naval Architecture and Marine Engineering, 2020, University of New Orleans

URL: https://scholarworks.uno.edu/td/2783

► Monte Carlo (MC) method is one of the commonly used methods to study stochastic statistical problems in the field of marine engineering. There is…
(more)

Subjects/Keywords: Trajectory prediction, Dropped cylinders, State space model, Offshore operations, Monte Carlo method, Unscented transformation method; Other Engineering

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Li, Y. (2020). Application of Monte Carlo (MC) method and Unscented Transformation (UT) method into Trajectory Prediction of a Dropped Cylinder in Two Dimensions. (Thesis). University of New Orleans. Retrieved from https://scholarworks.uno.edu/td/2783

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Li, Yi. “Application of Monte Carlo (MC) method and Unscented Transformation (UT) method into Trajectory Prediction of a Dropped Cylinder in Two Dimensions.” 2020. Thesis, University of New Orleans. Accessed April 18, 2021. https://scholarworks.uno.edu/td/2783.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Li, Yi. “Application of Monte Carlo (MC) method and Unscented Transformation (UT) method into Trajectory Prediction of a Dropped Cylinder in Two Dimensions.” 2020. Web. 18 Apr 2021.

Vancouver:

Li Y. Application of Monte Carlo (MC) method and Unscented Transformation (UT) method into Trajectory Prediction of a Dropped Cylinder in Two Dimensions. [Internet] [Thesis]. University of New Orleans; 2020. [cited 2021 Apr 18]. Available from: https://scholarworks.uno.edu/td/2783.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li Y. Application of Monte Carlo (MC) method and Unscented Transformation (UT) method into Trajectory Prediction of a Dropped Cylinder in Two Dimensions. [Thesis]. University of New Orleans; 2020. Available from: https://scholarworks.uno.edu/td/2783

Not specified: Masters Thesis or Doctoral Dissertation

5.
Hu, Junjun.
Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and *Unscented* *Transformation* Methods.

Degree: PhD, 2015, University of Oklahoma

URL: http://hdl.handle.net/11244/21722

► In the context of prediction science, the sources of uncertainty can be from the uncertainties of the experiments, modeling, model inputs, numerical analysis, etc. This…
(more)

Subjects/Keywords: Uncertainty Quantification; Polynomial Chaos Expansion; Unscented Transformation

…90
Chapter 6 Application of *Unscented* *Transformation* Approach… …131
Appendix D Performance of *Unscented* *Transformation*… …138
Appendix E Performance of Scaled *Unscented* *Transformation*… …*unscented* *transformation* (UT).
Using MC as the benchmark, two dynamical models are used… …extent,
further efforts are still needed.
The *unscented* *transformation* (UT) method is…

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Hu, J. (2015). Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/21722

Chicago Manual of Style (16^{th} Edition):

Hu, Junjun. “Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods.” 2015. Doctoral Dissertation, University of Oklahoma. Accessed April 18, 2021. http://hdl.handle.net/11244/21722.

MLA Handbook (7^{th} Edition):

Hu, Junjun. “Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods.” 2015. Web. 18 Apr 2021.

Vancouver:

Hu J. Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods. [Internet] [Doctoral dissertation]. University of Oklahoma; 2015. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/11244/21722.

Council of Science Editors:

Hu J. Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods. [Doctoral Dissertation]. University of Oklahoma; 2015. Available from: http://hdl.handle.net/11244/21722

Virginia Tech

6. Zhao, Junbo. A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control.

Degree: PhD, Electrical Engineering, 2018, Virginia Tech

URL: http://hdl.handle.net/10919/83423

► The enhancement of the reliability, security, and resiliency of electric power systems depends on the availability of fast, accurate, and robust dynamic state estimators. These…
(more)

Subjects/Keywords: Kalman filter; Robust statistics; Power system state estimation; Dynamic state estimation; Unscented transformation; Robust control theory; Estimation theory; Power system dynamics and control; Outliers; Cyber attacks; Phasor measurement units

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Zhao, J. (2018). A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83423

Chicago Manual of Style (16^{th} Edition):

Zhao, Junbo. “A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control.” 2018. Doctoral Dissertation, Virginia Tech. Accessed April 18, 2021. http://hdl.handle.net/10919/83423.

MLA Handbook (7^{th} Edition):

Zhao, Junbo. “A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control.” 2018. Web. 18 Apr 2021.

Vancouver:

Zhao J. A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10919/83423.

Council of Science Editors:

Zhao J. A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83423