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78 CHAPTER 4 NEURAL NETWORK BASED SYSTEM IDENTIFICATION validation are presented. Finally, the chapter is summarised in Section 4.4. 4.2 THE ARTIFICIAL NEURAL NETWORKS Artificial neural network (ANN) is a computational model consisting of a collection of interconnected neurons that have an ability to learn complex mapping (linear or non-linear) in the provided input-output data. It can also provide reliable predictions for new situations containing even noisy or partial information [Samarasinghe, 2007]. The mathematical computation of ANN has been largely constructed and inspired by the biological neurons in the human brain. Haykin [2009] states that the ANN resembles the human brain in two aspects: (1) the knowledge is gained by the networks through a learning process; and (2) the knowledge gained from the learning process is stored in the weighted interconnection between neurons or synaptic weights. The ANN can perform a diverse range of tasks including prediction/forecasting, function approximation, pattern classification and clustering. Throughout the ANN literature, there are a variety of ANN architectures that have been developed to achieve the mentioned tasks. The typical ANN types are shown in Figure 4.1 [Samarasinghe, 2007]. A single layer perceptron in Figure 4.1(a) is regarded as a linear classifier similar to simple and multiple discriminant function analysis in the field of statistics. The linear neuron shown in Figure 4.1(b) can function either as a linear classifier or predictor. In its prediction mode, the neuron’s predictive capabilities are similar to simple or multiple linear regression models in statistics. The Multi-Layered Perceptron (MLP) network shown in Figure 4.1(c) is the most popular neural network type which is suitable for non-linear classification and prediction tasks. The competitive network in Figure 4.1(d) is a network with unsupervised learning that is capable of finding clusters in the data. The self organising feature map (SOFM) competitive network shown in Figure 4.1(e) is also capable of finding unknown clusters in the data; and has the ability to preserve the spatial relationship of the data and clusters [Samarasinghe, 2007]. In Figure 4.1(f), two types of recurrent networks (Elman and Jordan network) are frequently used for time-series forecasting. These networks contain feedback links and memory/context units to capture the temporal data effects [Pham and Liu, 1993, Kalinli and Sagiroglu,
4.2 THE ARTIFICIAL NEURAL NETWORKS 79 (a) Linear Classifier (b) Linear classifier or predictor (c) Non-linear classifier or predictor (d) Unsupervised classifier (e) Unsupervised clustering/spatial relationship - Context Units (f) Time series forecasting Figure 4.1 Different types of NN modelling architectures: (a) Single-layer perceptron; (b) Linear neuron; (c) Multi-layer perceptron (MLP); (d) Competitive network; (e) Self-organising feature map (SOFM); and (f) Recurrent networks 2006]. In this chapter, the MLP, Elman network and their respective variants are used for the system identification application. All of these networks presented in Figure 4.1 contain many links connecting inputs to neurons and from neurons to outputs. These network connections are also popularly known as weights which are allowed to freely adapt to the training data by the learning algorithms. These weights are also regarded as free parameters as in regression modelling in statistics. Thus, this would make the neural networks similar to the parametric models involving the estimation of optimum parameters [Samarasinghe, 2007].
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The Development of Neural Network B
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ABSTRACT This thesis presents the d
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vii the MLP network, the prediction
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PUBLICATIONS The following is a lis
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xiv TABLE OF CONTENTS CHAPTER 4 CHA
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LIST OF FIGURES 1.1 Examples of typ
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LIST OF FIGURES xix 3.13 Overview o
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LIST OF FIGURES xxi 5.2 The predict
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LIST OF FIGURES xxiii 5.13 The pred
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LIST OF FIGURES xxv 7.1 The locatio
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LIST OF TABLES 3.1 The specificatio
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NOMENCLATURE AFCS Automatic Flight
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LIST OF TABLES xxxi NNARX QP RBF RC
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2 CHAPTER 1 INTRODUCTION Surveillan
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4 CHAPTER 1 INTRODUCTION is then de
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6 CHAPTER 1 INTRODUCTION the dynami
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8 CHAPTER 1 INTRODUCTION models [Sa
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10 CHAPTER 1 INTRODUCTION improved
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12 CHAPTER 1 INTRODUCTION Chapter 3
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Chapter 2 LITERATURE REVIEW The pur
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2.1 INTRODUCTION 17 • Inefficient
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2.1 INTRODUCTION 19 Figure 2.2 The
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2.2 HELICOPTER DYNAMICS MODELLING A
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5.2 OFF-LINE BASED SYSTEM IDENTIFIC
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.7 MODEL PERFORMANCE COMPARISON US
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5.8 SUMMARY 151 methods such as wei
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5.8 SUMMARY 153 would enable the im
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156 CHAPTER 6 NEURAL NETWORK BASED
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Chapter 7 FLIGHT CONTROL SYSTEM DES
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7.2 FLIGHT TESTS IMPLEMENTATION 185
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7.3 EXPERIMENTAL RESULTS 187 1 0.8
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7.3 EXPERIMENTAL RESULTS 189 Longit
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7.3 EXPERIMENTAL RESULTS 191 Pitch
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7.3 EXPERIMENTAL RESULTS 193 Flight
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7.3 EXPERIMENTAL RESULTS 195 140 Ya
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7.3 EXPERIMENTAL RESULTS 197 indica
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7.3 EXPERIMENTAL RESULTS 199 20 100
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7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
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7.3 EXPERIMENTAL RESULTS 203 mainta
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7.3 EXPERIMENTAL RESULTS 205 Yaw Ra
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7.4 SUMMARY 207 data measured from
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210 CHAPTER 8 CONCLUSIONS AND FUTUR
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212 CHAPTER 8 CONCLUSIONS AND FUTUR
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214 REFERENCES B. W. Bequette. Non-
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216 REFERENCES Konstantinos Dalamag
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218 REFERENCES Wayne Johnson. Helic
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220 REFERENCES B. Mettler, Mark B.
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222 REFERENCES V. R. Puttige and S.
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224 REFERENCES D.H. Shim. Hierarchi
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226 REFERENCES Nikos Vitzilaios and
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