The Development of Neural Network Based System Identification ...

xiv
TABLE OF CONTENTS
CHAPTER 4
CHAPTER 5
CHAPTER 6
NEURAL NETWORK BASED SYSTEM
IDENTIFICATION 77
4.1 Introduction 77
4.2 The Artificial Neural Networks 78
4.2.1 Multi-Layered Perceptron 81
4.2.2 Hybrid Multilayer Perceptron 84
4.2.3 Elman Network 86
4.3 System Identification with Neural Network 90
4.3.1 Collection of Flight Test Data 90
4.3.2 Neural Network Model Structure Selection 94
4.3.2.1 Lag Space Selection for Feed-Forward MLP
or HMLP Network 98
4.3.3 Off-line and Recursive Methods 99
4.3.4 Off-line based Neural Network Model Estimation 102
4.3.4.1 Jacobian Matrix Calculation 105
4.3.4.2 Training by Weight Regularisation 108
4.3.5 Recursive based Neural Network Model Estimation 111
4.3.6 Model Validation 115
4.4 Summary 117
NN BASED SYSTEM IDENTIFICATION: RESULTS
AND DISCUSSION 119
5.1 Introduction 119
5.2 Off-line based System Identification for MLP network 120
5.2.1 Improving Generalisation of Neural Network through
Regularisation 120
5.2.2 Model Structure Selection Results 124
5.3 Off-line based System Identification for HMLP network 133
5.4 Off-line based System Identification for Elman network 138
5.5 Model Performance Comparison Using Off-line Training 144
5.6 On-line System Identification 144
5.7 Model Performance Comparison Using Recursive Training 149
5.8 Summary 150
NEURAL NETWORK BASED PREDICTIVE
CONTROL SYSTEM 155
6.1 Introduction 155
6.2 NN based Approximate Predictive Control Principles 156
6.3 Principle of Instantaneous Linearisation 161
6.4 Non-minimal State Space Model Realisation 164
6.5 General Formulation of Augmented Model 166
6.6 Prediction from the State Space Models 168
6.7 Model Predictive Control Optimisation 170
6.8 Model Predictive Control with Constraints 172
6.8.1 The Hildreth’s Quadratic Programming Procedure 174