پروژه سیستم Monitoring system

اختصاصی از یارا فایل پروژه سیستم Monitoring system دانلود با لینک مستقیم و پرسرعت .

فرمت فایل : WORD (قابل ویرایش)

تعداد صفحات:67

پایان نامه کارشناسی
مهندسی کامپیوتر- سخت افزار

فهرست مطالب:
عنوان مطالب                                                    شماره صفحه

چکیده    1
مقدمه    2
الگوریتم وطرزکارماژول براساس مانیتورینگ دما    3
الگوریتم ماژول براساس فرکانس:    5
فصل اول:    7
معرفی مختصر قطعات به کار رفته در پروژه:    7
فصل دوم:    12
1-2 معرفی مختصر دستگاه:    12
فصل سوم:    14
3-1 بررسی برنامه میکرو کنترلر:    14
فصل چهارم:    46
کد های مربوط به برنامه نرم افزاری:    46
فصل پنجم:    50
مراجع:    67




 
چکیده:

این دستگاه بر اساس دمای محیط کار می کند که به وسیله ی یک سنسور, دما اندازه گیری می شود و روی lcd   نمایش  داده می شود.رنج دمایی که سنسور اندازه گیری می کند بین 0 تا  100 درجه است. به وسیله ی سنسوری دیگر هر جسم قابل حرکتی که به server room نزدیک شود را تشخیص داده و بر روی  lcdنمایش  می دهد.هر زمان دمای بحرانی از دمای محیط بیشتر شود یک فن محیط را خنک می کند.



مقدمه:

سیستم مانیتورینگ,یک سیستم امنیتی برای جلوگیری از دسترسی های غیر مجاز و محافظت وامنیت server room می باشد.این سیستم بسیار کارامد است و در بسیاری از مکان هایی که ما روزانه با ان سروکار داریم استفاده می شود از جمله ساختمان ها وادارات,... .
این سیستم شامل سه بخش است:1)ورودی:شامل یک سنسوری برای sense دمای محیط می باشدو ورودی دیگر سنسوری برای تشخیص حرکت می باشد.
2)پردازشگر:در این قسمت پردازش های لازم شامل خواندن سنسورها,نمایش,... .
 
3)خروجی:شامل یک lcd برای نمایش اطلاعات و دیگری خروجی پورت سریال برای نمایش بر روی کامپیوتر.

زمانی که دما توسط سنسور اندازه گیری می شود با دمای بحرانی که به صورت پیش فرض به دستگاه داده می شودو توسط دکمه های تعبیه شده قابل تغییر می باشد, مقایسه گردیده در صورتی که بیشتر باشد فنی روشن می شود که دمای
  server roomرا از دمای بحرانی پایین تر بیاورد.

Fundamentals of Power System Economics، مبانی اقتصادی سیستمهای قدرت،

اختصاصی از یارا فایل Fundamentals of Power System Economics، مبانی اقتصادی سیستمهای قدرت، دانلود با لینک مستقیم و پرسرعت .

291 صفحه

Fundamentals of Power System Economics, Wiley, 2004

Daniel Dirschen

مبانی اقتصادی سیستمهای قدرت

Power System State Estimation, Ali Abur,

اختصاصی از یارا فایل Power System State Estimation, Ali Abur, دانلود با لینک مستقیم و پرسرعت .

ایمیل فروشنده: [e-mail]

Power System State Estimation

Theory and Implementation

2004

Ali Abur

MARCEL DEKKER

فهرست کامل مطالب

Introduction
1.1 Operating States of a Power System
1.2 Power System Security Analysis
1.3 State Estimation
1.4 Summary
2 Weighted Least Squares State Estimation
2.1 Introductio
2.2 Component Modeling and Assumptions
2.2.1 Transmission Lines
2.2.2 Shunt Capacitors or Reactors
2.2.3 Tap Changing and Phase Shifting Transformers
2.2.4 Loads and Generators
2.3 Building the Network Model
2.4 Maximum Likelihood Estimation
2.4.1 Gaussian (Normal) Probability Density Function
2.4.2 The Likelihood Function
2.5 Measurement Model and Assumptions
2.6 WLS State Estimation Algorithm
2.6.1 The Measurement Function, A(a^)
2.6.2 The Measurement Jacobian, R
2.6.3 The Gain Matrix, G
2.6.4 Cholesky Decomposition of (7
2.6.5 Performing the Forward/Back Substitutions
2.7 Decoupled Formulation of the
WLS State Estimation
2.8 DC State Estimation Model
2.9 Problems
Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.
3 Alternative Formulations of the WLS State Estimation
3.1 Weaknesses of the Normal Equations Formulation
3.2 Orthogonal Factorization
3.3 Hybrid Method
3.4 Method of Peters and Wilkinson
3.5 Equality-Constrained WLS State Estimation
3.6 Augmented Matrix Approach
3.7 Blocked Formulation
3.8 Comparison of Techniques
3.9 Problems
References
4 Network Observability Analysis
4.1 Networks and Graphs
4.1.1 Graphs
4.1.2 Networks
4.2 NetworkMatrices
4.2.1 Branch to Bus Incidence Matrix
4.2.2 Fundamental Loop to Branch Incidence Matrix
4.3 LoopEquations
4.4 Methods of Observability Analysis
4.5 Numerical Method Based on the Branch Variable Formulation
4.5.1 New Branch Variables
4.5.2 Measurement Equations
4.5.3 Linearized Measurement Model
4.5.4 Observability Analysis
4.6 Numerical Method Based on the Nodal Variable Formulation
4.6.1 Determining the Unobservable Branches
4.6.2 Identification of Observable Islands
4.6.3 Measurement Placement to Restore
Observability
4.7 Topological Observability Analysis
Method
4.7.1 Topological Observability Algorithm
4.7.2 Identifying the Observable Islands
4.8 Determination of Critical Measurements
4.9 Measurement Design
4.10 Summary
4.11 Problems
References
Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.
5 Bad Data Detection and Identification
5.1 Properties of Measurement Residuals
5.2 Classification of Measurements
5.3 Bad Data Detection and IdentiRability
5.4 Bad Data Detection
5.4.1 Chi-squares x^ Distribution
5.4.2 Use of x^ Distribution for Bad Data Detection
5.4.3 x^-Test for Detecting Bad Data in WLS State Estimation
5.4.4 Use of Normalized Residuals for Bad Data
Detection
5.5 Properties of Normalized Residuals
5.6 Bad Data Identification
5.7 Largest Normalized Residual (r^aa) Test
5.7.1 Computational Issues
5.7.2 Strengths and Limitations of the r^ag Test
5.8 Hypothesis Testing Identification (HTI)
5.8.1 Statistical Properties of eg
5.8.2 Hypothesis Testing
5.8.3 Decision Rules
5.8.4 HTI Strategy Under Fixed /3
5.9 Summary
5.10 Problems
Reference
6 Robust State Estimation
6.1 Introductio
6.2 Robustness and Breakdown Points
6.3 Outliers and Leverage Points
6.3.1 Concept of Leverage Points
6.3.2 Identification of Leverage Measurements
6.4 M-Estimators
6.4.1 Estimation by Newton's Method
6.4.2 Iteratively Re-weighted Least Squares
Estimation
6.5 Least Absolute Value (LAV) Estimation
6.5.1 Linear Regression
6.5.2 LAV Estimation as an LP Problem
6.5.3 Simplex Based Algorithm
6.5.4 Interior Point Algorithm
6.6 Discussion
6.7 Problems
References
Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.
7 Network Parameter Estimation
7.1 Introduction
7.2 Influence of Parameter Errors on State
Estimation Results
7.3 Identification of Suspicious Parameters
7.4 Classification of Parameter Estimation
Methods
7.5 Parameter Estimation Based on Residua! Sensitivity Analysis
7.6 Parameter Estimation Based on State
Vector Augmentation
7.6.1 Solution Using Conventional Normal Equation
7.6.2 Solution Based on Kalman Filter Theory
7.7 Parameter Estimation Based on Historical Series of Data
7.8 Transformer Tap Estimation
7.9 Observability of Network Parameters
7.10 Discussion
7.11 Problems
References
8 Topology Error Processing
8.1 Introduction
8.2 Types of Topology Errors
8.3 Detection of Topology Errors
8.4 Classification of Methods for Topology Error Analysis
8.5 Preliminary Topology Validation
8.6 Branch Status Errors
8.6.1 Residual Analysis
8.6.2 State Vector Augmentation
8.7 Substation Configuration Errors
8.7.1 Inclusion of Circuit Breakers in the Network Model
8.7.2 WLAV Estimator
8.7.3 WLS Estimator
8.8 Substation Graph and Reduced Model
8.9 Implicit Substation Model: State and
Status Estimation
8.10 Observability Analysis Revisited
8.11 Problems
References
9 State Estimation Using Ampere Measurements
9.1 Introduction
9.2 Modeling of Ampere Measurements
9.3 Difficulties in Using Ampere
Measurements
Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.
9.4 Inequality-Constrained State Estimation
9.5 Heuristic Determination of F-# Solution Uniqueness
9.6 Algorithmic Determination of Solution
Uniqueness
9.6.1 Procedure Based on the Residual Covariance Matrix
9.6.2 Procedure Based on the Jacobian Matrix
9.7 Identification of Nonuniquely Observable Branches
9.8 Measurement Classification and Bad Data Identific
9.8.1 LS Estimation
9.8.2 LAV Estimation
9.9 Problems
References
Appendix A Review of Basic Statistics
A.I Random Variables
A.2 The Distribution Function (d.f.), F(x)
A.3 The Probability Density Function (p.d.f), f(x)
A.4 Continuous Joint Distributions
A.5 Independent Random Variables
A.6 Conditional Distributions
A.7 Expected Value
A.8 Variance
A.9 Median
A.10 Mean Squared Error
A.11 Mean Absolute Error
A.12 Covariance
A.13 Normal Distribution
A.14 Standard Normal Distribution
A.15 Properties of Normally Distributed Random Variables
A.16 Distribution of Sample Mean
A.17 Likelihood Function and Maximum
Likelihood Estimator
A.17.1 Properties of MLE's
A.18 Central Limit Theorem for the Sample Mean
Appendix B Review of Sparse Linear Equation Solution
B.I Solution by Direct Methods
B.2 Elementary Matrices
B.3 LU Factorization Using Elementary Matrices
B.3.1 Grout's Algorithm
B.3.2 Dooh'ttle's Algorithm
B.3.3 Factorization of Sparse Symmetric Matrice
B.3.4 Ordering Sparse Symmetric Matrices
B.4 Factorization Path Graph
Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.
B.5 Sparse Forward/Back Substitutions
B.6 Solution of Modified Equations
B.6.1 Partial Refactorization
B.6.2 Compensation
B.7 Sparse Inverse
B.8 Orthogonal Factorization
B.9 Storage and Retrieval of Sparse Matrix Elements
B.10 Inserting and/or Deleting Elements in a Linked List
B.10.1 Adding a Nonzero Element
B.10.2 Deleting a Nonzero Element
References

Power System Relaying, Horowitz, third edition, S.Horowitz

اختصاصی از یارا فایل Power System Relaying, Horowitz, third edition, S.Horowitz دانلود با لینک مستقیم و پرسرعت .

Power System Relaying, Horowitz, third edition, S.Horowitz

Wiley, 2008

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