Lecture Notes Table of Contents (PDF)
Available lecture notes are listed below.
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) |