Gaussian process approach for modelling of nonlinear systems

2009 ◽  
Vol 22 (4-5) ◽  
pp. 522-533 ◽  
Author(s):  
Gregor Gregorčič ◽  
Gordon Lightbody
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
Process Approach

Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
Continuous Time ◽  
Process Models ◽  
Gaussian Process Models

Gaussian process approach for metric learning

2019 ◽  
Vol 87 ◽  
pp. 17-28 ◽  
Author(s):  
Ping Li ◽  
Songcan Chen
Keyword(s):  
Gaussian Process ◽  
Metric Learning ◽  
Process Approach

scholarly journals Bayesian Joint Input-State Estimation for Nonlinear Systems

Vibration ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 281-303
Author(s):  
Timothy J. Rogers ◽  
Keith Worden ◽  
Elizabeth J. Cross
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
State Estimation ◽  
State Space ◽  
Mean Squared Error ◽  
Duffing Oscillator ◽  
State Space Model ◽  
Input State ◽  
Force Model ◽  
Internal States

This work suggests a solution for joint input-state estimation for nonlinear systems. The task is to recover the internal states of a nonlinear oscillator, the displacement and velocity of the system, and the unmeasured external forces applied. To do this, a Gaussian process latent force model is developed for nonlinear systems. The model places a Gaussian process prior over the unknown input forces for the system, converts this into a state-space form and then augments the nonlinear system with these additional hidden states. To perform inference over this nonlinear state-space model a particle Gibbs approach is used combining a “Particle Gibbs with Ancestor Sampling” Markov kernel for the states and a Metropolis-Hastings update for the hyperparameters of the Gaussian process. This approach is shown to be effective in a numerical case study on a Duffing oscillator where the internal states and the unknown forcing are recovered, each with a normalised mean-squared error less than 0.5%. It is also shown how this Bayesian approach allows uncertainty quantification of the estimates of the states and inputs which can be invaluable in further engineering analyses.

Gaussian Process Approach to Buried Object Size Estimation in GPR Images

2010 ◽  
Vol 7 (1) ◽  
pp. 141-145 ◽  
Author(s):  
Edoardo Pasolli ◽  
Farid Melgani ◽  
Massimo Donelli
Keyword(s):  
Gaussian Process ◽  
Process Approach ◽  
Object Size ◽  
Size Estimation ◽  
Buried Object

A multi-objective Gaussian process approach for optimization and prediction of carbonization process in carbon fiber production under uncertainty

2019 ◽  
Vol 2 (3) ◽  
pp. 444-455 ◽  
Author(s):  
Milad Ramezankhani ◽  
Bryn Crawford ◽  
Hamid Khayyam ◽  
Minoo Naebe ◽  
Rudolf Seethaler ◽  
...  
Keyword(s):  
Carbon Fiber ◽  
Gaussian Process ◽  
Process Approach ◽  
Fiber Production ◽  
Carbonization Process ◽  
Multi Objective
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