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Identification, Estimation, and Learning >> Content Detail



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

 








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