Implementation of gaussian process models for non-linear system identification
نویسنده
چکیده
................................................................................................................... ii Acknowledgements ........................................................................................................ iv 1) Introduction ................................................................................................................ 1 1.1) Original Contributions ......................................................................................... 3 1.2) Thesis Outline ...................................................................................................... 5 2) Nonlinear System Identification................................................................................ 7 2.1) The System Identification Process....................................................................... 7 2.2) Role of Prior Knowledge ................................................................................... 10 2.2.1) Overall Modelling Objectives ................................................................... 11 2.2.2) Knowledge of System Characteristics....................................................... 12 2.2.3) Knowledge of Empirical Data or Experimental Conditions ..................... 12 2.3) Experimental Design.......................................................................................... 13 2.3.1) Which Measurements ? ............................................................................ 14 2.3.2) Excitation Signals...................................................................................... 15 2.3.2.1) Active Learning ................................................................................ 17 2.4) Pre-processing Data – Creating the Training Data Set ...................................... 18 2.5) Choice of Model Architecture ........................................................................... 20 2.5.1) Linear and Nonlinear Models.................................................................... 20 2.5.2) Parametric and Nonparametric Models..................................................... 22 2.5.3) Linear Dynamic Models............................................................................ 23 2.5.3.1) Linear to Nonlinear Dynamic Models .............................................. 25 2.5.4) Nonlinear Dynamic Models ...................................................................... 27 2.5.5) Neural Networks........................................................................................ 30 2.5.5.1) Multilayer Perceptron (MLP) Network ............................................ 31 2.5.5.2) Radial Basis Function (RBF) Network............................................. 34 2.5.5.2.1) Normalised RBF Networks...................................................... 37 2.5.6) Multiple Model Networks ......................................................................... 38 2.5.6.1) Local Model Networks ..................................................................... 40 2.5.6.1.1) Off-Equilibrium Dynamics ...................................................... 43
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