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Packard, Norman H. Breeden, Joseph Lynn 2011-05-07T13:47:33Z 2011-05-07T13:47:33Z 10000-01-01 1992 No single state space reconstruction of experimental data is optimal for all scientific endeavors. This thesis is all about the coordinates used in reconstruction, the goals of reconstruction, and the criteria used to select optimal reconstructions. A learning algorithm is described, which when given a space of possible coordinates and an optimality criterion selects the best state space reconstruction. This method is applied to specific problems: modeling nonuniformly sampled data, computing the inferable dimension of a data set, and model-based control. Made available in DSpace on 2011-05-07T13:47:33Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9236400.pdf: 5553985 bytes, checksum: 2621ee17c7ebf3b16bc3a6484c0d8623 (MD5) Previous issue date: 1992 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:59:12Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:27:54-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI9236400 (UMI)AAI9236400 http://hdl.handle.net/2142/22671 eng Copyright 1992 Breeden, Joseph Lynn Physics, General Physics, Astronomy and Astrophysics Optimal representation of experimental data text Physics Physics University of Illinois at Urbana-Champaign Dissertation Ph.D.