Multiple Gaussian Process Models for Direct Time Series Forecasting
スポンサーリンク
概要
- 論文の詳細を見る
- 2011-05-01
著者
-
Hachino Tomohiro
Department Of Electrical And Electronics Engineering Kagoshima University
-
Kadirkamanathan Visakan
Department Of Automatic Control And Systems Engineering University Of Sheffield
-
Kadirkamanathan Visakan
Department Of Automatic Control And Systems Engineering The University Of Sheffield
関連論文
- On-line identification of nonlinear systems using Volterra polynomial basis function neural networks
- Structure Selection and Identification of Hammerstein Type Nonlinear Systems Using Automatic Choosing Function Model and Genetic Algorithm(Nonlinear Theory and its Applications)
- Multiple Gaussian Process Models for Direct Time Series Forecasting
- On-line Identification Method of Continuous-Time Nonlinear Systems Using Radial Basis Function Network Model Adjusted by Genetic Algorithm(Nonlinear Theory and its Applications)
- Identification of Continuous-time Nonlinear Systems by Using a Gaussian Process Model
- An Augmented Automatic Choosing Control of Observer Type for Nonlinear Systems with Linear Measurement and Its Application to Power Systems