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This course provides a broad theoretical basis for system identification, estimation, and learning.
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English
English [CC]
FREE
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Description
Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike’s information criterion, experiment design, and model validation.
Course content
- Introduction Unlimited
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- Recursive Least Square (RLS) Algorithms Unlimited
- Properties of RLS Unlimited
- Random Processes, Active Noise Cancellation Unlimited
- Discrete Kalman Filter-1 Unlimited
- Discrete Kalman Filter-2 Unlimited
- Continuous Kalman Filter Unlimited
- Extended Kalman Filter Unlimited
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- Prediction Modeling of Linear Systems Unlimited
- Model Structure of Linear Time-invariant Systems Unlimited
- Time Series Data Compression, Laguerre Series Expansion Unlimited
- Non-linear Models, Function Approximation Theory, Radial Basis Functions Unlimited
- Neural Networks Unlimited
- Error Back Propagation Algorithm Unlimited
- Perspective of System Identification, Frequency Domain Analysis Unlimited
- Informative Data Sets and Consistency Unlimited
- Informative Experiments: Persistent Excitation Unlimited
- Asymptotic Distribution of Parameter Estimates Unlimited
- Experiment Design, Pseudo Random Binary Signals (PRBS) Unlimited
- Maximum Likelihood Estimate, Cramer-Rao Lower Bound and Best Unbiased Estimate Unlimited
- Information Theory of System Identification: Kullback-Leibler Information Distance, Akaike’s Information Criterion Unlimited
Instructor
Massachusetts Institute of Technology
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