國立虎尾科技大學 |

Language: English

Back

Chaos, Observability and Symplectic ...

University of California, San Diego.

Chaos, Observability and Symplectic Structure in Optimal Estimation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Chaos, Observability and Symplectic Structure in Optimal Estimation./
Author:	Rey, Daniel.
Description:	1 online resource (185 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
Subject:	Biophysics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355313659

Chaos, Observability and Symplectic Structure in Optimal Estimation.
Rey, Daniel.

Chaos, Observability and Symplectic Structure in Optimal Estimation. - 1 online resource (185 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Observation, estimation and prediction are universal challenges that become especially difficult when the system under consideration is dynamical and chaotic. Chaos injects dynamical noise into the estimation process that must be suppressed to satisfy the necessary conditions for success: namely, synchronization of the estimate and the observed data. The ability to control the growth of errors is constrained by the spatiotemporal resolution of the observations, and often exhibits critical thresholds below which the probability of success becomes effectively zero. This thesis examines the connections between these limits and basic issues of complexity, conditioning, and instability in the observation and forecast models. The results suggest several new ideas to improve the collaborative design of combined observation, analysis, and forecast systems. Among these, the most notable is perhaps the fundamental role that symplectic structure plays in the remarkable observational efficiency of Kalman-based estimation methods.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355313659Subjects--Topical Terms:

581576
Biophysics.
Index Terms--Genre/Form:

554714
Electronic books.

Chaos, Observability and Symplectic Structure in Optimal Estimation.
LDR:02344ntm a2200361Ki 4500 001 909790
005 20180426091046.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355313659
035 $a (MiAaPQ)AAI10281245
035 $a (MiAaPQ)ucsd:16475
035 $a AAI10281245
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Rey, Daniel. $3 1180744
245 1 0 $a Chaos, Observability and Symplectic Structure in Optimal Estimation.
264 0 $c 2017
300 $a 1 online resource (185 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Henry DI Abarbanel.
502 $a Thesis (Ph.D.) $c University of California, San Diego $d 2017.
504 $a Includes bibliographical references
520 $a Observation, estimation and prediction are universal challenges that become especially difficult when the system under consideration is dynamical and chaotic. Chaos injects dynamical noise into the estimation process that must be suppressed to satisfy the necessary conditions for success: namely, synchronization of the estimate and the observed data. The ability to control the growth of errors is constrained by the spatiotemporal resolution of the observations, and often exhibits critical thresholds below which the probability of success becomes effectively zero. This thesis examines the connections between these limits and basic issues of complexity, conditioning, and instability in the observation and forecast models. The results suggest several new ideas to improve the collaborative design of combined observation, analysis, and forecast systems. Among these, the most notable is perhaps the fundamental role that symplectic structure plays in the remarkable observational efficiency of Kalman-based estimation methods.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Biophysics. $3 581576
650 4 $a Computer science. $3 573171
650 4 $a Applied mathematics. $3 1069907
655 7 $a Electronic books. $2 local $3 554714
690 $a 0786
690 $a 0984
690 $a 0364
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Diego. $b Physics. $3 1180745
773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10281245 $z click for full text (PQDT)

based on 0 review(s)

Multimedia

Reviews

Add a review and share your thoughts with other readers

Export

pickup library