Lecture Notes Table of Contents (PDF)
Available lecture notes are listed below.
Lecture files.
| Lec # |
Topics |
| 1 |
Introduction (PDF 1) (PDF 2) |
| Part I: Estimation |
| 2 |
Recursive Least Square (RLS) Algorithms (PDF) |
| 3 |
Properties of RLS (PDF) |
| 4 |
Random Processes, Active Noise Cancellation (PDF) |
| 5 |
Discrete Kalman Filter-1 (PDF) |
| 6 |
Discrete Kalman Filter-2 (PDF) |
| 7 |
Continuous Kalman Filter (PDF) |
| 8 |
Extended Kalman Filter (PDF) |
| Part 2: Representation and Learning |
| 9 |
Prediction Modeling of Linear Systems (PDF) |
| 10 |
Model Structure of Linear Time-invariant Systems (PDF) |
| 11 |
Time Series Data Compression, Laguerre Series Expansion (PDF) |
| 12 |
Non-linear Models, Function Approximation Theory, Radial Basis Functions (PDF) |
| 13 |
Neural Networks (PDF) |
| 14 |
Error Back Propagation Algorithm (PDF) |
| Part 3: System Identification |
| 15 |
Perspective of System Identification, Frequency Domain Analysis (PDF) |
| 16 |
Informative Data Sets and Consistency (PDF) |
| 17 |
Informative Experiments: Persistent Excitation (PDF) |
| 18 |
Asymptotic Distribution of Parameter Estimates (PDF) |
| 19 |
Experiment Design, Pseudo Random Binary Signals (PRBS) (PDF) |
| 20 |
Maximum Likelihood Estimate, Cramer-Rao Lower Bound and Best Unbiased Estimate (PDF) |
| 21 |
Information Theory of System Identification: Kullback-Leibler Information Distance, Akaike's Information Criterion (PDF) |
