Identification of models for linear and nonlinear non-causal data structures 1 Uwe Kruger and 2 Jochen Einbeck 1 Department of Mechanical & Industrial.

Slides:



Advertisements
Similar presentations
4.1 Introduction to Matrices
Advertisements

Identity and Inverse Matrices
Component Analysis (Review)
Modelling and Identification of dynamical gene interactions Ronald Westra, Ralf Peeters Systems Theory Group Department of Mathematics Maastricht University.
13 May 2009Instructor: Tasneem Darwish1 University of Palestine Faculty of Applied Engineering and Urban Planning Software Engineering Department Introduction.
Algebraic, transcendental (i.e., involving trigonometric and exponential functions), ordinary differential equations, or partial differential equations...
© 2005 Baylor University Slide 1 Fundamentals of Engineering Analysis EGR 1302 Unit 1, Lecture B Approximate Running Time - 24 minutes Distance Learning.
Mathematics. Matrices and Determinants-1 Session.
MF-852 Financial Econometrics
Jonathan Richard Shewchuk Reading Group Presention By David Cline
Psychology 202b Advanced Psychological Statistics, II January 25, 2011.
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 12 System of Linear Equations.
Matrix Operations. Matrix Notation Example Equality of Matrices.
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 14 Elimination Methods.
1 Neural Nets Applications Vectors and Matrices. 2/27 Outline 1. Definition of Vectors 2. Operations on Vectors 3. Linear Dependence of Vectors 4. Definition.
NUU Department of Electrical Engineering Linear Algebra---Meiling CHEN1 Lecture 28 is positive definite Similar matrices Jordan form.
MOHAMMAD IMRAN DEPARTMENT OF APPLIED SCIENCES JAHANGIRABAD EDUCATIONAL GROUP OF INSTITUTES.
Basic Mathematics for Portfolio Management. Statistics Variables x, y, z Constants a, b Observations {x n, y n |n=1,…N} Mean.
Linear regression models in matrix terms. The regression function in matrix terms.
Modern Navigation Thomas Herring
Arithmetic Operations on Matrices. 1. Definition of Matrix 2. Column, Row and Square Matrix 3. Addition and Subtraction of Matrices 4. Multiplying Row.
CE 311 K - Introduction to Computer Methods Daene C. McKinney
1 Statistical Analysis Professor Lynne Stokes Department of Statistical Science Lecture 5QF Introduction to Vector and Matrix Operations Needed for the.
 Definition: An Identity Matrix is a square matrix that, when multiplied by another matrix, equals the same matrix.  Form: In the Identity Matrix the.
Finite Mathematics Dr. Saeid Moloudzadeh Using Matrices to Solve Systems of Equations 1 Contents Algebra Review Functions and Linear Models.
Dr. Mubashir Alam King Saud University. Outline Systems of Linear Equations (6.1) Matrix Arithmetic (6.2) Arithmetic Operations (6.2.1) Elementary Row.
CHAPTER 2 MATRIX. CHAPTER OUTLINE 2.1 Introduction 2.2 Types of Matrices 2.3 Determinants 2.4 The Inverse of a Square Matrix 2.5 Types of Solutions to.
Linear Prediction Coding of Speech Signal Jun-Won Suh.
ECE 8443 – Pattern Recognition LECTURE 03: GAUSSIAN CLASSIFIERS Objectives: Normal Distributions Whitening Transformations Linear Discriminants Resources.
ICS201 Lecture 12 : Gentle Introduction to Computer Graphics II King Fahd University of Petroleum & Minerals College of Computer Science & Engineering.
10.4 Matrix Algebra 1.Matrix Notation 2.Sum/Difference of 2 matrices 3.Scalar multiple 4.Product of 2 matrices 5.Identity Matrix 6.Inverse of a matrix.
Matrices. Definitions  A matrix is an m x n array of scalars, arranged conceptually as m rows and n columns.  m is referred to as the row dimension.
Matrix Algebra and Regression a matrix is a rectangular array of elements m=#rows, n=#columns  m x n a single value is called a ‘scalar’ a single row.
Company LOGO Chapter 1: Matrix, Determinant, System of linear equations Module 1: Matrix Natural Science Department Example 1: The following rectangular.
Time-Varying Angular Rate Sensing for a MEMS Z-Axis Gyroscope Mohammad Salah †, Michael McIntyre †, Darren Dawson †, and John Wagner ‡ Mohammad Salah †,
Thomas Knotts. Engineers often: Regress data  Analysis  Fit to theory  Data reduction Use the regression of others  Antoine Equation  DIPPR.
2014. Engineers often: Regress data  Analysis  Fit to theory  Data reduction Use the regression of others  Antoine Equation  DIPPR We need to be.
SUPA Advanced Data Analysis Course, Jan 6th – 7th 2009 Advanced Data Analysis for the Physical Sciences Dr Martin Hendry Dept of Physics and Astronomy.
PROCESS MODELLING AND MODEL ANALYSIS © CAPE Centre, The University of Queensland Hungarian Academy of Sciences Statistical Model Calibration and Validation.
Discrete Mathematics 1 Kemal Akkaya DISCRETE MATHEMATICS Lecture 16 Dr. Kemal Akkaya Department of Computer Science.
1 Matrix Algebra and Random Vectors Shyh-Kang Jeng Department of Electrical Engineering/ Graduate Institute of Communication/ Graduate Institute of Networking.
Fundamentals of Engineering Analysis
Relations, Functions, and Matrices Mathematical Structures for Computer Science Chapter 4 Copyright © 2006 W.H. Freeman & Co.MSCS Slides Relations, Functions.
CSCI 171 Presentation 9 Matrix Theory. Matrix – Rectangular array –i th row, j th column, i,j element –Square matrix, diagonal –Diagonal matrix –Equality.
3.6 Solving Systems Using Matrices You can use a matrix to represent and solve a system of equations without writing the variables. A matrix is a rectangular.
10.4 Matrix Algebra 1.Matrix Notation 2.Sum/Difference of 2 matrices 3.Scalar multiple 4.Product of 2 matrices 5.Identity Matrix 6.Inverse of a matrix.
Similar diagonalization of real symmetric matrix
Colorado Center for Astrodynamics Research The University of Colorado 1 STATISTICAL ORBIT DETERMINATION Kalman Filter with Process Noise Gauss- Markov.
Numerical Analysis – Data Fitting Hanyang University Jong-Il Park.
I. Quadratic Forms and Canonical Forms Def 1 : Definition 2 : If linear operations.
Matrices. Variety of engineering problems lead to the need to solve systems of linear equations matrixcolumn vectors.
10.4 Matrix Algebra. 1. Matrix Notation A matrix is an array of numbers. Definition Definition: The Dimension of a matrix is m x n “m by n” where m =
A very brief introduction to Matrix (Section 2.7) Definitions Some properties Basic matrix operations Zero-One (Boolean) matrices.
1 Matrix Math ©Anthony Steed Overview n To revise Vectors Matrices.
Colorado Center for Astrodynamics Research The University of Colorado 1 STATISTICAL ORBIT DETERMINATION Statistical Interpretation of Least Squares ASEN.
Charles University FSV UK STAKAN III Institute of Economic Studies Faculty of Social Sciences Institute of Economic Studies Faculty of Social Sciences.
Thomas F. Edgar (UT-Austin) RLS – Linear Models Virtual Control Book 12/06 Recursive Least Squares Parameter Estimation for Linear Steady State and Dynamic.
MTH108 Business Math I Lecture 20.
ECE 3301 General Electrical Engineering
Calibrated estimators of the population covariance
CH 5: Multivariate Methods
Regression.
Assoc. Prof. Dr. Peerapol Yuvapoositanon
Random Error Propagation
The regression model in matrix form
Random Error Propagation
Matrices Introduction.
Chapter 4 Mathematical Expectation.
Linear Discrimination
Topic 11: Matrix Approach to Linear Regression
Presentation transcript:

Identification of models for linear and nonlinear non-causal data structures 1 Uwe Kruger and 2 Jochen Einbeck 1 Department of Mechanical & Industrial Engineering, Sultan Qaboos University, P.O. Box 33, Muscat, Oman 2 Department of Mathematical Sciences, Durham University, DH1 3LE, U.K.

Uwe Kruger and Jochen Einbeck Identification of models for linear and nonlinear non-causal data structures Durham, 7. May, 2013 Slide 2 Presentation Outline 1.Linear non-causal data structures with simple diagonal error covariance matrix containing unknown and equal diagonal elements 2.Linear non-causal data structures with known symmetric and positive definite error covariance matrix 3.Linear non-causal data structures with unknown diagonal error covariance matrix containing unequal and unknown diagonal elements 4.Estimating of fractal dimension for nonlinear non-causal data structures.

Uwe Kruger and Jochen Einbeck Identification of models for linear and nonlinear non-causal data structures Durham, 7. May, 2013 Linear non-causal data structures diagonal error covariance matrix with unknown but identical diagonal elements (i) Slide 3

Uwe Kruger and Jochen Einbeck Identification of models for linear and nonlinear non-causal data structures Durham, 7. May, 2013 Slide 4 Linear non-causal data structures diagonal error covariance matrix with unknown but identical diagonal elements (ii)

Uwe Kruger and Jochen Einbeck Identification of models for linear and nonlinear non-causal data structures Durham, 7. May, 2013 Slide 5 Linear non-causal data structures symmetric and positive definite error covariance matrix that is known (i)

Uwe Kruger and Jochen Einbeck Identification of models for linear and nonlinear non-causal data structures Durham, 7. May, 2013 Slide 6 Linear non-causal data structures symmetric and positive definite error covariance matrix that is known (ii)

Uwe Kruger and Jochen Einbeck Identification of models for linear and nonlinear non-causal data structures Durham, 7. May, 2013 Slide 7 Linear non-causal data structures unknown diagonal error covariance matrix with unequal diagonal elements (i)

Uwe Kruger and Jochen Einbeck Identification of models for linear and nonlinear non-causal data structures Durham, 7. May, 2013 Slide 8 Linear non-causal data structures unknown diagonal error covariance matrix with unequal diagonal elements (ii)

Uwe Kruger and Jochen Einbeck Identification of models for linear and nonlinear non-causal data structures Durham, 7. May, 2013 Slide 9 Linear non-causal data structures unknown diagonal error covariance matrix with unequal diagonal elements (iii)