Gaussian Processes for Modelling of Dynamic Non-linear Systems

نویسندگان

  • Gregor Gregorčič
  • Gordon Lightbody
چکیده

Parametric multiple model techniques have recently been proposed for the modelling of non–linear systems and use in nonlinear control. Research effort has focused on issues such as the selection of the structure, constructive learning techniques, computational issues, the curse of dimensionality, off–equilibrium behavior etc. To reduce these problems, the use of non–parametrical modelling approaches have been proposed. This paper introduces the Gaussian process prior approach for the modelling of non–linear dynamic systems. Issues such as selection of the input space dimension and multi–step ahead prediction are discussed in this paper. The Gaussian process modelling technique is demonstrated on the simulated example of the non–linear hydraulic system.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...

متن کامل

Design of Instrumentation Sensor Networks for Non-Linear Dynamic Processes Using Extended Kalman Filter

This paper presents a methodology for design of instrumentation sensor networks in non-linear chemical plants. The method utilizes a robust extended Kalman filter approach to provide an efficient dynamic data reconciliation. A weighted objective function has been introduced to enable the designer to incorporate each individual process variable with its own operational importance. To enhance...

متن کامل

Simulation of multivariate non-gaussian autoregressive time series with given autocovariance and marginals

A semi-analytic method is proposed for the generation of realizations of a multivariate process of a given linear correlation structure and marginal distribution. This is an extension of a similar method for univariate processes, transforming the autocorrelation of the non-Gaussian process to that of a Gaussian process based on a piece-wise linear marginal transform from non-Gaussian to Gaussia...

متن کامل

Predictive control with Gaussian process models

This paper describes model-based predictive control based on Gaussian processes. Gaussian process models provide a probabilistic nonparametric modelling approach for black-box identification of non-linear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. The Gaussian processes can highlight areas of the in...

متن کامل

Application of varying parameters modelling with Gaussian processes

This paper describes an application of the method for modelling nonlinear dynamic systems from measurement data. The method merges the linear local model blending approach in the velocity-based linearisation form with Bayesian Gaussian process modelling. The new FixedStructure Gaussian Process model has a predetermined linear model structure with varying and probabilistic parameters represented...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2002