رفع مشکل عدم شبکه و ارور Not Registered On The Network برای گوشی i9300
اختصاصی از یارا فایل رفع مشکل عدم شبکه و ارور Not Registered On The Network برای گوشی i9300 دانلود با لینک مستقیم و پرسرعت .
رفع مشکل عدم شبکه و ارور Not Registered On The Network برای گوشی i9300
با سلام خدمت دوستان عزیز . امروز یک فایل کامل به همراه آموزش کامل در رابطه با مشکل پریدن آنتن یا تخریب سریال گوشی i9300 را برای شما در سایت گذاشته ایم . که میتوانید بسیار راحت این فایل کم حجم را دانلود و استفاده نمایید .
این مشکلات معمولا به دلیل فلش کردن و یا آپدیت و دانگرید به وجود می آید که با این فایل کاملا حل میشود .
کاملا تست شده توسط خودم . در صورت بروز هر گونه مشکل و یا درخواست راهنمایی با شماره بالا تماس بگیرید .
کتاب ارزشمندگشایش هیجان انگیزپروازسوسماران بالدار برای سیاه Fly the Pterodactyl
اختصاصی از یارا فایل کتاب ارزشمندگشایش هیجان انگیزپروازسوسماران بالدار برای سیاه Fly the Pterodactyl دانلود با لینک مستقیم و پرسرعت .
Fly the Pterodactyl
گشایش هیجان انگیز پروازسوسماران بالدار برای سیاه
توسط اریک شیلر
فرمت: PDF
تعداد صفحات: 401
ناشر: (Ishi Press (April 20, 2012
اورجینال با قابلیت کپی و چاپ
978-4871874830:ISBN
راسته هایی از سوسماران بالدار (1 G6، 2 ... Bg7، 3 ... c5 ... Qa5) یکی از هیجان انگیز ترین گشایش جدید درشطرنج است که تنها در قرن 21st محبوبیت واقعی را به دست آورد. با ساختار شبیه به سیسیلی اژدها، که سیاه متکی به فیل بسیار قدرتمند و وزیر فعال خود، فشار بر پوزیسیون سفید میآورد. بسیاری از استادان بزرگ این گشایش را بازی کرده و در بیش از 20،000 بازی دیده شده است، استاد فیده اریک شیلر این گشایش را در تمام شکوه خود با تجزیه و تحلیل دقیق ارائه داده است. بازی های گویای کامل این کتاب شما را به آنچه نیاز به دانستن است پیدا می کنید و سیاه را مجبور به مقابله با حریفان خود درمراحل اولیه بازی میکند. نویسنده حتی در برابر استادان بزرگ بازی کرده است بنابراین این گشایش هیجان انگیز کشف و از آن برای گیج کردن حریفان خود استفاده شده است.
The Pterodactyl (1…g6, 2…Bg7, 3…c5 and an early …Qa5) is one of the most exciting new chess openings. Used experimentally in the 1950s and 1960s, it has only gained true popularity in the 21st century. With the structure closely resembling the Sicilian Dragon, Black relies on a very powerful Bishop and active Queen to put pressure on White's position. The opening has been played by many grandmasters and the formation has been seen in over 20,000 games, yet until now there has been no in-depth study of the Pterodactyl. FIDE master Eric Schiller presents the opening in all its glory with detailed opening analysis and over. The complete illustrative games in this book you will find all you need to know to take up it as black and confront your opponents from the very earliest stages of the game. The author has played the opening frequently and successfully even against Grandmasters and has many insights into past that were geared more unexplored. At the same time, this remains a new opening, so there is still room for plenty of original play. So explore this exciting opening and use it to confound your opponents no matter how they choose to open as White.
The switching function analysis of power electronic circuits
اختصاصی از یارا فایل The switching function analysis of power electronic circuits دانلود با لینک مستقیم و پرسرعت .
The switching function analysis of power electronic circuits
IET 2008
سرفصلها
Part 1 The switching function 1
1 The switching function: Application and properties 3
1.1 Introduction 3
1.2 Application of the switching function technique 3
1.3 Properties of the switching function 6
2 Voltage–current relations in switched circuits 17
2.1 Single switch 18
2.2 Parallel switches 19
2.3 Parallel switched-resistors 20
2.4 Switched-inductors 21
2.5 Parallel switched-capacitors 24
2.6 Kirchoff’s First Law (current law) 27
2.7 Kirchoff’s Second Law (voltage) 27
2.8 Superposition theorem in switched circuits 29
2.9 Current sharing in a parallel RC switched network 31
3 Pulse width modulation 35
3.1 Sinusoidally modulated PWM signal – unipolar 36
3.2 The rectified sine-wave PWM signal 39
3.3 The PWM signal of a composite function 43
3.4 PWM sine-wave – bipolar square wave modulation 45
Part 2 AC to DC conversion 49
4 Analysis of the single phase ac to dc phase controlled converter
with R–L load 51
4.1 Introduction 51
4.2 Mathematical modelling 51
4.3 Analysis 55
5 The single phase full-wave diode rectifier – RC load 65
5.1 Introduction 65
5.2 Mathematical modelling 65
vi Contents
5.3 Analysis 72
5.4 Neutral current in three phase systems 77
6 The three-phase half-wave phase controlled converter 83
6.1 Introduction 83
6.2 Mathematical modelling of the three-phase half-wave
phase controlled converter 83
6.3 Analysis 91
6.4 Results 98
7 The three-phase full-wave phase controlled rectifier 101
7.1 Introduction 101
7.2 The mathematical modelling of the three-phase
full-wave controlled rectifier circuit 101
7.3 Analysis of three-phase full-wave phase controlled
rectifier 111
8 Overlap in ac to dc three-phase converters 121
8.1 Introduction 121
8.2 Operation and modes 122
8.3 Analysis 132
Part 3 DC to DC converters 135
9 The step down converter 137
9.1 Introduction 137
9.2 Mathematical modelling of the step down converter 137
10 The step up or boost converter 145
10.1 Introduction 145
10.2 Mathematical modelling of the dc to dc step up
(boost) converter 145
10.3 Analysis 152
11 The buck boost dc to dc converter 163
11.1 Introduction 163
11.2 Mathematical modelling of the buck boost converter 164
11.3 Analysis of the buck boost converter 168
12 The CUK dc to dc converter 175
12.1 Introduction 175
12.2 Mathematical modelling of the CUK dc to dc
converter 175
12.3 Analysis of the CUK dc to dc converter 180
13 The PWM full bridge dc to dc converter 187
13.1 Introduction 187
13.2 Operation and modes of the PWM full bridge dc to dc
converter: bipolar operation 188
13.3 Analysis of the PWM full bridge dc to dc converter:
bipolar operation 189
13.4 Operation and modes of the PWM full bridge
dc to dc converter: unipolar operation 193
Contents vii
13.5 Analysis of the PWM full bridge dc to dc converter:
unipolar operation 197
Part 4 Frequency changers 203
14 Three by three matrix converter 205
14.1 Introduction 205
14.2 Operation and mathematical model 206
14.3 The modes of operation and the switching functions 208
14.4 Analysis of the matrix converter as a three-phase to
three-phase system 211
14.5 The matrix converter as an ac to dc voltage converter 218
15 The single pulse PWM inverter 223
15.1 Introduction 223
15.2 Operation and modes of the circuit 223
15.3 The mathematical model and analysis 229
16 The sinusoidally PWM inverter 235
16.1 Introduction 235
16.2 Mathematical modelling 235
16.3 Analysis 241
17 The envelope cyclo-converter 245
17.1 Introduction 245
17.2 The mathematical model 245
17.3 The switching functions 246
Part 5 Active filters 249
18 The thyristor-controlled reactor 251
18.1 Introduction 251
18.2 The single reactor arrangement 251
18.3 The two reactor arrangement 257
19 The switched capacitor active filters 263
19.1 Introduction 263
19.2 The general model for the switched-capacitor active
filters 263
19.3 The switching functions 265
19.4 The line current 266
19.5 The double-switch double-capacitor 266
19.6 Reactive power generation 266
20 The inverter configuration active filter 271
20.1 Introduction 271
20.2 Operation and analysis 271
21 Single phase rectification with active line shaping 275
21.1 Mathematical modelling of the active shaping circuit 275
Discussion 289
References 291
Index 293
دانلود پایان نامه دکترای زبان انگلیسی The Semantics and Pragmatics of Demonstratives in English and Arabic
اختصاصی از یارا فایل دانلود پایان نامه دکترای زبان انگلیسی The Semantics and Pragmatics of Demonstratives in English and Arabic دانلود با لینک مستقیم و پرسرعت .
دانلود پایان نامه دکترای زبان انگلیسی با موضوع The Semantics and Pragmatics of Demonstratives in English and Arabic که شامل 272 صفحه و بشرح زیر میباشد:
نوع فایل : PDF قابل ویرایش
Abstract
This research investigates the semantics and pragmatics of demonstratives in two
languages, English and Arabic, within the framework of relevance theory. The study applies
the fundamental distinction between ‘conceptual’ and ‘procedural’ semantics in an attempt to
account for the various instantiations of such referring expressions in the two languages. I
argue that demonstratives play a crucial role in aligning the discourse models of the speaker
and hearer by encoding procedural semantics instructing the hearer to maintain or create a
joint level of attention to the intended referent as opposed to other referential candidates.
Following Diessel (2006), I take it that this notion of joint attention subsumes all the
cognitive and functional roles played by demonstratives in discourse. I also argue that
demonstratives encode a (pro)concept of distance which falls under the scope of the
attention-directing procedure, thus creating the internal contrast between the intended referent
and other candidate referents. Within this proposal, I discuss how demonstratives can
contribute to both the explicit and the implicit levels of meaning by virtue of the interaction
between their encoded semantics and the context in a relevance-driven framework. Compared
to other referring expressions or no referring expression at all, the role of a demonstrative
achieves relevance on the implicit level. It can either highlight a certain aspect of the referent,
or encourage the creation of weak implicatures, or signal a certain cognitive/emotional
attitude towards the referent. The study is supported by an analysis of corpus data from both
languages in order to supplement theoretical proposals with attested evidence.
I further extend my analysis to include two areas. First, I discuss cases of self-repair in
spoken English discourse which involves the definite article and demonstratives. By linking
the notion of self-repair to that of optimal relevance, I shed some light on the semantic and
pragmatic differences between these two referring expressions. Second, I extend my analysis
to include other forms of demonstratives in Arabic and explore their semantic and pragmatic
behaviour in discourse. I propose a procedural account for the three forms attentional haa,
kadhaalik and haakadhaa, arguing that their contribution goes well beyond that of mere
demonstrative reference to that of being discourse markers encoding procedural constraints
on interpretation. I also investigate some alternative syntactic structures where
demonstratives occur, arguing that the stylistic effect of emphasis which they give rise to can
be explained in terms of relevant cognitive effects.Contents
Contents
Abstract ii
Acknowledgements iii
List of Tables and Figures vii
Arabic Transcription Notations viii
List of Abbreviations ix
Chapter 1: Introduction 10
1.1 Aims of the research 10
1.2 Theoretical 14
1.3 Data 15
1.3.1 The English Corpus (ICE-GB) 16
1.3.2 The Arabic Corpus (NEMLAR) 19
1.4 Structure of the thesis 23
Chapter 2: Previous studies 26
2.1 Reference 26
2.2 Demonstratives in English 29
2.3 Demonstratives in Arabic 33
2.4 Cognitive approaches 39
2.4.1 The Givenness Hierarchy 40
2.4.2 Demonstratives and interaction 45
2.3.3 Demonstratives and joint attention 48
2.5 Summary 52
Chapter 3: Relevance theory 54
3.1 Relevance theory 54
3.1.1 Relevance theory and communication 54
3.1.2 Inference and understanding 57
3.1.3 Explicating and implicating 61
3.1.4 Relevance and reference 65
3.2 Concepts and procedures 70
3.2.1 The conceptual-procedural distinction and the explicit-implicit distinction .......... 72
3.2.2 The conceptual-procedural distinction and truth 76
3.2.3 Types of concepts 78
3.3 Summary 81
Chapter 4. The semantics and pragmatics of demonstratives 83
4.1 The semantics of demonstratives 83
4.1.1 What do demonstratives encode? 84
4.1.2 Demonstratives and 86
4.1.3 Demonstratives and attention 92
4.1.4 Distance, attention and relevance 101
4.2 The interpretation of demonstratives 107
4.2.1 Demonstratives and explicit content 107
4.2.2 Demonstratives and implicit content 125
4.2.3 First-mention demonstratives 136
4.3 Summary 142
Chapter 5. Extending the analysis: Demonstratives and Self-Repair in English 143
5.1 Demonstratives and self-repair 143
5.1.1 The definite article and demonstratives 144
5.1.2 Self-repair and relevance 151
5.1.3 The this/that 154
5.1.4 This/that the 157
5.1.5 Distal or proximal? 161
5.2 Summary 164
Chapter 6. Extending the analysis: Other forms of demonstratives in Modern Standard Arabic165
6.1 The morphology and semantics of demonstratives in MSA 165
6.1.1 Demonstrative forms in MSA and varieties of Arabic 167
6.1.2 Arabic and procedural meaning 171
6.2 Case studies 177
6.2.1 Attentional haa: procedure and attention 177
6.2.1.1 Approaches to attentional haa 179
6.2.1.2 The relevance of attentional haa 183
6.2.2 kadhaalik: demonstrative or discourse 191
6.2.2.1 A distinction 192
6.2.2.2 kadhaalik as a demonstrative 195
6.2.2.3 kadhaalik as a discourse marker 205
6.2.3 haakadhaa: deictic, anaphoric and discourse functions 219
6.2.3.1 A distinction 221
6.2.3.3 haakadhaa in discourse marker uses 227
6.3 A note on demonstratives and the interpretation of emphasis 235
6.3.1 Noun + demonstrative 236
6.3.2 Proper noun + demonstrative 242
6.3.3 Demonstrative + 3rd person pronoun + noun 249
6.4 Summary 254
Chapter 7. Conclusion 255
7.1 Summary 255
7.2 Future research 259
References 262
List of Tables and Figures
Table 1: The English corpus chosen for this study from the ICE-GB 19
Table 2: The Arabic corpus chosen for this study from NEMLAR 23
Table 3: Demonstrative forms in MSA 34
Table 4: The lexical semantics of the pronoun she (from Nicolle 1997: 49) 104
Table 5: Demonstratives in MSA according to Holes (2004) 169
Table 6: Proximal demonstrative forms in Arabic dialects 169
Table 7: Distal demonstrative forms in Arabic dialects 170
Table 8: Number of instances of kadhaalik in the corpus 206
Table 9: Number of instances of haakadhaa in the corpus 221
Figure 1: Proposed semantic analysis for English/Arabic demonstratives 12
Figure 2: Screen shot of that concordance in the ICE-GB using the ICECUP 18
Figure 3: Screen shot of haadhihi concordance in NEMLAR using LOLO 22
Figure 4: The Givenness Hierarchy according to Gundel et al. (1993) 41
Figure 5: Four types of meaning according to Wilson & Sperber 74
Figure 6: The morphology of haadhaa 177
Figure 7: The morphology of kadhaalik 191
Figure 8: The morphology of 220
Nonlinear System Identification, NARMAX method in the time, frequency and spatio-temporal domains, 2013, Wiley
اختصاصی از یارا فایل Nonlinear System Identification, NARMAX method in the time, frequency and spatio-temporal domains, 2013, Wiley دانلود با لینک مستقیم و پرسرعت .
Nonlinear System Identification, NARMAX method in the time, frequency and spatio-temporal domains
2013, Wiley
table of contents
1 Introduction 1
1.1 Introduction to System Identification 1
1.1.1 System Models and Simulation 1
1.1.2 Systems and Signals 3
1.1.3 System Identification 3
1.2 Linear System Identification 3
1.3 Nonlinear System Identification 5
1.4 NARMAX Methods 7
1.5 The NARMAX Philosophy 8
1.6 What is System Identification For? 9
1.7 Frequency Response of Nonlinear Systems 11
1.8 Continuous-Time, Severely Nonlinear, and Time-Varying Models
and Systems 12
1.9 Spatio-temporal Systems 13
1.10 Using Nonlinear System Identification in Practice and Case Study Examples 13
References 14
2 Models for Linear and Nonlinear Systems 17
2.1 Introduction 17
2.2 Linear Models 18
2.2.1 Autoregressive Moving Average with Exogenous Input Model 18
2.2.2 Parameter Estimation for Linear Models 20
2.3 Piecewise Linear Models 22
2.3.1 Spatial Piecewise Linear Models 23
2.3.2 Models with Signal-Dependent Parameters 26
2.3.3 Remarks on Piecewise Linear Models 29
2.4 Volterra Series Models 30
viii Contents
2.5 Block-Structured Models 31
2.5.1 Parallel Cascade Models 32
2.5.2 Feedback Block-Structured Models 32
2.6 NARMAX Models 33
2.6.1 Polynomial NARMAX Model 35
2.6.2 Rational NARMAX Model 37
2.6.3 The Extended Model Set Representation 39
2.7 Generalised Additive Models 40
2.8 Neural Networks 41
2.8.1 Multi-layer Networks 41
2.8.2 Single-Layer Networks 42
2.9 Wavelet Models 45
2.9.1 Dynamic Wavelet Models 46
2.10 State-Space Models 48
2.11 Extensions to the MIMO Case 49
2.12 Noise Modelling 49
2.12.1 Noise-Free 50
2.12.2 Additive Random Noise 50
2.12.3 Additive Coloured Noise 50
2.12.4 General Noise 51
2.13 Spatio-temporal Models 52
References 53
3 Model Structure Detection and Parameter Estimation 61
3.1 Introduction 61
3.2 The Orthogonal Least Squares Estimator and the Error Reduction Ratio 64
3.2.1 Linear-in-the-Parameters Representation 64
3.2.2 The Matrix Form of the Linear-in-the-Parameters
Representation 65
3.2.3 The Basic OLS Estimator 65
3.2.4 The Matrix Formulation of the OLS Estimator 67
3.2.5 The Error Reduction Ratio 68
3.2.6 An Illustrative Example of the Basic OLS Estimator 69
3.3 The Forward Regression OLS Algorithm 70
3.3.1 Forward Regression with OLS 72
3.3.2 An Illustrative Example of Forward Regression with OLS 77
3.3.3 The OLS Estimation Engine and Identification Procedure 78
3.4 Term and Variable Selection 79
3.5 OLS and Sum of Error Reduction Ratios 80
3.5.1 Sum of Error Reduction Ratios 82
3.5.2 The Variance of the s-Step-Ahead Prediction Error 82
3.5.3 The Final Prediction Error 83
3.5.4 The Variable Selection Algorithm 83
3.6 Noise Model Identification 84
3.6.1 The Noise Model 84
3.6.2 A Simulation Example with Noise Modelling 87
Contents ix
3.7 An Example of Variable and Term Selection for a Real Data Set 87
3.8 ERR is Not Affected by Noise 94
3.9 Common Structured Models to Accommodate Different Parameters 95
3.10 Model Parameters as a Function of Another Variable 98
3.10.1 System Internal and External Parameters 98
3.10.2 Parameter-Dependent Model Structure 98
3.10.3 Modelling Auxetic Foams – An Example of External
Parameter-Dependent Model Identification 99
3.11 OLS and Model Reduction 100
3.12 Recursive Versions of OLS 102
References 102
4 Feature Selection and Ranking 105
4.1 Introduction 105
4.2 Feature Selection and Feature Extraction 106
4.3 Principal Components Analysis 107
4.4 A Forward Orthogonal Search Algorithm 108
4.4.1 The Basic Idea of the FOS-MOD Algorithm 108
4.4.2 Feature Detection and Ranking 109
4.4.3 Monitoring the Search Procedure 111
4.4.4 Illustrative Examples 112
4.5 A Basis Ranking Algorithm Based on PCA 113
4.5.1 Principal Component-Derived Multiple Regression 113
4.5.2 PCA-Based MFROLS Algorithms 114
4.5.3 An Illustrative Example 115
References 117
5 Model Validation 119
5.1 Introduction 119
5.2 Detection of Nonlinearity 121
5.3 Estimation and Test Data Sets 123
5.4 Model Predictions 124
5.4.1 One-Step-Ahead Prediction 124
5.4.2 Model Predicted Output 126
5.5 Statistical Validation 127
5.5.1 Correlation Tests for Input–Output Models 128
5.5.2 Correlation Tests for Time Series Models 132
5.5.3 Correlation Tests for MIMO Models 133
5.5.4 Output-Based Tests 134
5.6 Term Clustering 135
5.7 Qualitative Validation of Nonlinear Dynamic Models 137
5.7.1 Poincaré Sections 139
5.7.2 Bifurcation Diagrams 139
5.7.3 Cell Maps 140
5.7.4 Qualitative Validation in Nonlinear System Identification 140
References 145
x Contents
6 The Identification and Analysis of Nonlinear Systems
in the Frequency Domain 149
6.1 Introduction 149
6.2 Generalised Frequency Response Functions 151
6.2.1 The Volterra Series Representation of Nonlinear Systems 153
6.2.2 Generalised Frequency Response Functions 156
6.2.3 The Relationship Between GFRFs and Output Response
of Nonlinear Systems 157
6.2.4 Interpretation of the Composition of the Output Frequency Response
of Nonlinear Systems 162
6.2.5 Estimation and Computation of GFRFs 165
6.2.6 The Analysis of Nonlinear Systems Using GFRFs 176
6.3 Output Frequencies of Nonlinear Systems 184
6.3.1 Output Frequencies of Nonlinear Systems under
Multi-tone Inputs 185
6.3.2 Output Frequencies of Nonlinear Systems for General Inputs 187
6.4 Nonlinear Output Frequency Response Functions 191
6.4.1 Definition and Properties of NOFRFs 192
6.4.2 Evaluation of NOFRFs 195
6.4.3 Damage Detection Using NARMAX Modelling and NOFRF-Based
Analysis 196
6.5 Output Frequency Response Function of Nonlinear Systems 202
6.5.1 Definition of the OFRF 203
6.5.2 Determination of the OFRF 203
6.5.3 Application of the OFRF to Analysis of Nonlinear Damping
for Vibration Control 207
References 213
7 Design of Nonlinear Systems in the Frequency Domain – Energy
Transfer Filters and Nonlinear Damping 217
7.1 Introduction 217
7.2 Energy Transfer Filters 218
7.2.1 The Time and Frequency Domain Representation
of the NARX Model with Input Nonlinearity 220
7.2.2 Energy Transfer Filter Designs 222
7.3 Energy Focus Filters 240
7.3.1 Output Frequencies of Nonlinear Systems with Input Signal Energy
Located in Two Separate Frequency Intervals 241
7.3.2 The Energy Focus Filter Design Procedure and an Example 245
7.4 OFRF-Based Approach for the Design of Nonlinear Systems in the
Frequency Domain 249
7.4.1 OFRF-Based Design of Nonlinear Systems
in the Frequency Domain 249
7.4.2 Design of Nonlinear Damping in the Frequency Domain for
Vibration Isolation: An Experimental Study 251
References 259
Contents xi
8 Neural Networks for Nonlinear System Identification 261
8.1 Introduction 261
8.2 The Multi-layered Perceptron 263
8.3 Radial Basis Function Networks 264
8.3.1 Training Schemes for RBF Networks 266
8.3.2 Fixed Kernel Centres with a Single Width 266
8.3.3 Limitation of RBF Networks with a Single Kernel Width 268
8.3.4 Fixed Kernel Centres and Multiple Kernel Widths 269
8.4 Wavelet Networks 270
8.4.1 Wavelet Decompositions 271
8.4.2 Wavelet Networks 272
8.4.3 Limitations of Fixed Grid Wavelet Networks 273
8.4.4 A New Class of Wavelet Networks 274
8.5 Multi-resolution Wavelet Models and Networks 277
8.5.1 Multi-resolution Wavelet Decompositions 277
8.5.2 Multi-resolution Wavelet Models and Networks 280
8.5.3 An Illustrative Example 282
References 284
9 Severely Nonlinear Systems 289
9.1 Introduction 289
9.2 Wavelet NARMAX Models 291
9.2.1 Nonlinear System Identification Using Wavelet Multi-resolution
NARMAX Models 292
9.2.2 A Strategy for Identifying Nonlinear Systems 299
9.3 Systems that Exhibit Sub-harmonics and Chaos 301
9.3.1 Limitations of the Volterra Series Representation 301
9.3.2 Time Domain Analysis 302
9.4 The Response Spectrum Map 305
9.4.1 Introduction 305
9.4.2 Examples of the Response Spectrum Map 306
9.5 A Modelling Framework for Sub-harmonic and Severely
Nonlinear Systems 313
9.5.1 Input Signal Decomposition 314
9.5.2 MISO NARX Modelling in the Time Domain 317
9.6 Frequency Response Functions for Sub-harmonic Systems 320
9.6.1 MISO Frequency Domain Volterra Representation 320
9.6.2 Generating the GFRFs from the MISO Model 322
9.7 Analysis of Sub-harmonic Systems and the Cascade to Chaos 326
9.7.1 Frequency Domain Response Synthesis 326
9.7.2 An Example of Frequency Domain Analysis for
Sub-harmonic Systems 332
References 334
10 Identification of Continuous-Time Nonlinear Models 337
10.1 Introduction 337
xii Contents
10.2 The Kernel Invariance Method 338
10.2.1 Definitions 338
10.2.2 Reconstructing the Linear Model Terms 342
10.2.3 Reconstructing the Quadratic Model Terms 346
10.2.4 Model Structure Determination 348
10.3 Using the GFRFs to Reconstruct Nonlinear Integro-differential Equation
Models Without Differentiation 352
10.3.1 Introduction 352
10.3.2 Reconstructing the Linear Model Terms 355
10.3.3 Reconstructing the Quadratic Model Terms 358
10.3.4 Reconstructing the Higher-Order Model Terms 361
10.3.5 A Real Application 364
References 367
11 Time-Varying and Nonlinear System Identification 371
11.1 Introduction 371
11.2 Adaptive Parameter Estimation Algorithms 372
11.2.1 The Kalman Filter Algorithm 372
11.2.2 The RLS and LMS Algorithms 375
11.2.3 Some Practical Considerations for the KF, RLS,
and LMS Algorithms 376
11.3 Tracking Rapid Parameter Variations Using Wavelets 376
11.3.1 A General Form of TV-ARX Models Using Wavelets 376
11.3.2 A Multi-wavelet Approach for Time-Varying Parameter Estimation 377
11.4 Time-Dependent Spectral Characterisation 378
11.4.1 The Definition of a Time-Dependent Spectral Function 378
11.5 Nonlinear Time-Varying Model Estimation 380
11.6 Mapping and Tracking in the Frequency Domain 381
11.6.1 Time-Varying Frequency Response Functions 381
11.6.2 First and Second-Order TV-GFRFs 382
11.7 A Sliding Window Approach 388
References 389
12 Identification of Cellular Automata and N-State Models
of Spatio-temporal Systems 391
12.1 Introduction 391
12.2 Cellular Automata 393
12.2.1 History of Cellular Automata 393
12.2.2 Discrete Lattice 393
12.2.3 Neighbourhood 394
12.2.4 Transition Rules 396
12.2.5 Simulation Examples of Cellular Automata 399
12.3 Identification of Cellular Automata 402
12.3.1 Introduction and Review 402
12.3.2 Polynomial Representation 403
12.3.3 Neighbourhood Detection and Rule Identification 405
Contents xiii
12.4 N-State Systems 414
12.4.1 Introduction to Excitable Media Systems 414
12.4.2 Simulation of Excitable Media 415
12.4.3 Identification of Excitable Media Using a CA Model 419
12.4.4 General N-State Systems 424
References 427
13 Identification of Coupled Map Lattice and Partial Differential
Equations of Spatio-temporal Systems 431
13.1 Introduction 431
13.2 Spatio-temporal Patterns and Continuous-State Models 432
13.2.1 Stem Cell Colonies 433
13.2.2 The Belousov–Zhabotinsky Reaction 434
13.2.3 Oxygenation in Brain 434
13.2.4 Growth Patterns 435
13.2.5 A Simulated Example Showing Spatio-temporal Chaos
from CML Models 435
13.3 Identification of Coupled Map Lattice Models 437
13.3.1 Deterministic CML Models 437
13.3.2 The Identification of Stochastic CML Models 454
13.4 Identification of Partial Differential Equation Models 458
13.4.1 Model Structure 458
13.4.2 Time Discretisation 459
13.4.3 Nonlinear Function Approximation 459
13.5 Nonlinear Frequency Response Functions for Spatio-temporal Systems 466
13.5.1 A One-Dimensional Example 467
13.5.2 Higher-Order Frequency Response Functions 468
References 471
14 Case Studies 473
14.1 Introduction 473
14.2 Practical System Identification 474
14.3 Characterisation of Robot Behaviour 478
14.3.1 Door Traversal 478
14.3.2 Route Learning 482
14.4 System Identification for Space Weather and the Magnetosphere 484
14.5 Detecting and Tracking Iceberg Calving in Greenland 493
14.5.1 Causality Detection 494
14.5.2 Results 495
14.6 Detecting and Tracking Time-Varying Causality for EEG Data 498
14.6.1 Data Acquisition 499
14.6.2 Causality Detection 500
14.6.3 Detecting Linearity and Nonlinearity 504
14.7 The Identification and Analysis of Fly Photoreceptors 505
14.7.1 Identification of the Fly Photoreceptor 506
14.7.2 Model-Based System Analysis in the Time
and Frequency Domain 507
xiv Contents
14.8 Real-Time Diffuse Optical Tomography Using RBF Reduced-Order Models
of the Propagation of Light for Monitoring Brain Haemodynamics 514
14.8.1 Diffuse Optical Imaging 515
14.8.2 In-vivo Real-Time 3-D Brain Imaging Using Reduced-Order
Forward Models 517
14.9 Identification of Hysteresis Effects in Metal Rubber Damping Devices 522
14.9.1 Dynamic Modelling of Metal Rubber Damping Devices 523
14.9.2 Model Identification of a Metal Rubber Specimen 526
14.10 Identification of the Belousov–Zhabotinsky Reaction 528
14.10.1 Data Acquisition 529
14.10.2 Model Identification 530
14.11 Dynamic Modelling of Synthetic Bioparts 534
14.11.1 The Biopart and the Experiments 535
14.11.2 NARMAX Model of the Synthetic Biopart 536
14.12 Forecasting High Tides in the Venice Lagoon 539
14.12.1 Time Series Forecasting Problem 540
14.12.2 Water-Level Modelling and High-Tide Forecasting 541
References 543
Index 5