Lecture notes and recordings for ECE5560: System Identification
To play any of the lecture recording files (below), QuickTime is required. This software may be downloaded (free) from http://www.apple.com/quicktime/download/. Each of the recordings is on the order of 20 to 30 minutes long, and between about 40 and 50 Mb in size. If you are not registered at UCCS to take this course for credit, and if you wish to do so, please contact Dr. Gregory Plett using the information provided in the Section 0 notes. This course can be taken at the graduate level as part of the Masters of Science in Electrical Engineering option in Battery Controls. See the IDEATE web site for more details.- Course introduction and syllabus. [PDF]
- Introduction to System Identification. [PDF]
- Unit-Pulse-Response Identification. [PDF]
- 2.1: Continuous-time linear time-invariant systems.
- 2.2: The importance of the unit-pulse response.
- 2.3: Direct approach to finding unit-pulse response.
- 2.4: Scalar random variables.
- 2.5: Vector random variables.
- 2.6: Properties of jointly-distributed random variables.
- 2.7: Vector random (stochastic) processes.
- 2.8: Back to system identification.
- Frequency-Response Identification. [PDF]
- 3.1: Transfer operator and frequency function.
- 3.2: Direct identification of frequency function.
- 3.3: Identification of frequency function via Fourier analysis.
- 3.4: Empirical transfer function estimates.
- 3.5: Spectra of quasi-stationary signals.
- 3.6: Smoothed periodograms.
- 3.7: Frequency filtering.
- 3.8: Blackman-Tukey estimate.
- 3.9: ETFE and SPA toolbox commands.
- Transfer-Function Identification. [PDF]
- 4.1: Introduction to transfer functions.
- 4.2: Some examples of time responses versus pole locations.
- 4.3: Bode plots from discrete-time transfer functions.
- 4.4: System ID with transfer-function models.
- 4.5: Initial model structure selection.
- 4.6: Fitting parameterized model: Simulation or prediction?.
- 4.7: Fitting parameterized model: Cost function.
- 4.8: Solving the linear least-squares ARX problem.
- 4.9: Nonlinear optimization.
- 4.10: Generic nonlinear optimization example.
- 4.11: Toolbox methods (1): Frequency response.
- 4.12: Toolbox methods (2): Impulse response, residuals.
- 4.13: Toolbox methods (3): Model validation using correlations.
- 4.14: Toolbox methods (4): ARX model size.
- 4.15: An example with nonwhite noise.
- 4.16: Model quality: Bias error.
- 4.17: Bias problems in ARX WLS solution.
- State-Space Identification, Noiseless Data. [PDF]
- 5.1: Introduction to state-space models.
- 5.2: Working with state-space systems.
- 5.3: Discrete-time Markov parameters.
- 5.4: Discrete-time controllability and observability.
- 5.5: State-space realization problem.
- 5.6: Quadratic forms.
- 5.7: Matrix gain.
- 5.8: Singular value decomposition (SVD) of a matrix.
- 5.9: Moore-Penrose pseudo-inverse.
- 5.10: Back to Ho-Kalman.
- 5.11: Fibonacci example.
- 5.12: Eigensystem Realization Algorithm (ERA).
- 5.13: The subspace methods, data matrices.
- 5.14: Geometric projections that we will need.
- 5.15: Deterministic subspace system identification.
- 5.16: Data matrices for continuous-time realization.
- 5.17: Simple continuous-time realization.
- 5.18: Numeric problems with simple algorithm.
- 5.19: Proof of better conditioning.
- 5.20: Improved continuous-time system ID.
- State-Space Identification, Noisy Data. [PDF]
- 6.1: Stochastic identification via subspace methods.
- 6.2: Step 1: Some statistical relationships.
- 6.3: Step 2a: Kalman-filter covariance.
- 6.4: Step 2b: Kalman-filter state.
- 6.5: Step 3: Geometric properties of stochastic systems.
- 6.6: Step 4: Computing the system matrices.
- 6.7: Combined deterministic-stochastic identification.
- 6.8: Step 1a: Kalman-filter covariance.
- 6.9: Step 1b: Kalman-filter state.
- 6.10: Step 2a: Orthogonal projection for combined systems (A,B-1).
- 6.11: Step 2b: Orthogonal projection for combined systems....
- 6.12: Step 2c: Orthogonal projection for combined systems....
- 6.13: Step 3: Oblique projections for combined systems.
- 6.14: Step 4a: Computing the system matrices A, C.
- 6.15: Step 4b: Computing the system matrices B, D.
- Advanced Topics in System Identification. [PDF]
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1420 Austin Bluffs Pkwy, Colorado Springs, CO USA 80918
719-255-8227 (UCCS), 800-990-8227
