Dynamic GP models: an overview and recent developments

机译：动态GP模型：概述和最新动态

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Various methods can be used for nonlinear, dynamic-system identification and Gaussian process (GP) model is a relatively recent one. The GP model is an example of a probabilistic, nonparametric model with uncertainty predictions. It possesses several interesting features like model predictions contain the measure of confidence. Further, the model has a small number of training parameters, a facilitated structure determination and different possibilities of including prior knowledge about the modelled system. The framework for the identification of dynamic systems with GP models are presented and an overview of recent advances in the research of dynamic-system identification with GP models and its applications are given.

机译：非线性，动态系统识别可以使用多种方法，而高斯过程（GP）模型是一种相对较新的方法。 GP模型是具有不确定性预测的概率性非参数模型的一个示例。它具有几个有趣的功能，例如模型预测包含置信度。此外，该模型具有少量的训练参数，便利的结构确定以及包括关于建模系统的先验知识的不同可能性。介绍了用GP模型识别动态系统的框架，并概述了用GP模型识别动态系统的最新进展及其应用。

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来源
《Recent researches in applied mathematics and economics》|2012年|38-43|共6页
会议地点 Athens(GR);Athens(GR)
作者
JUS KOCIJAN;
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作者单位

Jozef Stefan Institute Jamova 39, Ljubljana SLOVENIA University of Nova Gorica Vipavska 13, Nova Gorica SLOVENIA;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
nonlinear-system identification; gaussian process models; dynamic systems; regression; control sys tems; fault detection; bayesian filtering;

机译：非线性系统辨识高斯过程模型；动态系统；回归控制系统故障检测；贝叶斯滤波;

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Dynamic GP models: an overview and recent developments

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