scholarly journals Parameter Estimation of a Gaussian Mixture Model for Wind Power Forecast Error by Riemann L-BFGS Optimization

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 38892-38899 ◽  
Author(s):  
Fuchao Ge ◽  
Yuntao Ju ◽  
Zhinan Qi ◽  
Yi Lin
Keyword(s):  
Parameter Estimation ◽  
Wind Power ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Forecast Error ◽  
Gaussian Mixture ◽  
Wind Power Forecast

Gaussian mixture model-based neural network for short-term wind power forecast

2017 ◽  
Vol 27 (6) ◽  
pp. e2320 ◽  
Author(s):  
Gary W. Chang ◽  
Heng-Jiu Lu ◽  
Ping-Kui Wang ◽  
Yung-Ruei Chang ◽  
Yee-Der Lee
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Short Term ◽  
Model Based ◽  
Wind Power Forecast

Cost Function Based on Gaussian Mixture Model for Parameter Estimation of a Chaotic Circuit with a Hidden Attractor

2014 ◽  
Vol 24 (01) ◽  
pp. 1450010 ◽  
Author(s):  
Seng-Kin Lao ◽  
Yasser Shekofteh ◽  
Sajad Jafari ◽  
Julien Clinton Sprott
Keyword(s):  
Parameter Estimation ◽  
Cost Function ◽  
State Space ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Real System ◽  
Hidden Attractor ◽  
The Real ◽  
Likelihood Score

In this paper, we introduce a new chaotic system and its corresponding circuit. This system has a special property of having a hidden attractor. Systems with hidden attractors are newly introduced and barely investigated. Conventional methods for parameter estimation in models of these systems have some limitations caused by sensitivity to initial conditions. We use a geometry-based cost function to overcome those limitations by building a statistical model on the distribution of the real system attractor in state space. This cost function is defined by the use of a likelihood score in a Gaussian Mixture Model (GMM) which is fitted to the observed attractor generated by the real system in state space. Using that learned GMM, a similarity score can be defined by the computed likelihood score of the model time series. The results show the adequacy of the proposed cost function.

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