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Identification, Estimation, and Learning >> Content Detail



Lecture Notes



Lecture Notes

Lecture Notes Table of Contents (PDF)

Available lecture notes are listed below.


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

 








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