Work

Convex optimization in identification of stable non-linear state space models

Year: 2010

Type: conference-paper

Abstract: A new framework for nonlinear system identification is presented in terms of optimal fitting of stable nonlinear state space equations to input/output/state data, with a performance objective defined as a measure of robustness of the simulation error with respect to equation errors. Basic definitions and analytical results are presented. The utility of the method is illustrated on a simple simulation example as well as experimental recordings from a live neuron.

Source: Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes

DOI: 10.1109/cdc.2010.5718114

Authors: Mark M. Tobenkin, Ian R. Manchester, Jennifer Wang, Alexandre Megretski, Russ Tedrake

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2129644659

FWCI: 3.409

Cites: 14

Cited by: 27

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