1.

Record Nr.

UNINA9910452026503321

Autore

Ferrell Robyn <1960->

Titolo

Copula [[electronic resource] ] : sexual technologies, reproductive powers / / Robyn Ferrell

Pubbl/distr/stampa

Albany, : State University of New York Press, c2006

ISBN

0-7914-8177-8

1-4294-1353-0

Descrizione fisica

1 online resource (191 p.)

Collana

SUNY series in gender theory

Disciplina

306.874/3/01

Soggetti

Sex role - Philosophy

Feminist theory

Motherhood - Philosophy

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Bibliographic Level Mode of Issuance: Monograph

Nota di bibliografia

Includes bibliographical references (p. 163-172) and index.

Nota di contenuto

The maternal in its natural habitat -- Brave new world -- Reproducing technology -- Conceiving of feminism -- Feminism is a kind of time -- The lore of the father -- The figure of the copula -- The body as material event -- The technology of genre.



2.

Record Nr.

UNINA9910906301403321

Autore

Levanony David

Titolo

Stochastic Lagrangian Adaptation / / by David Levanony, Peter E. Caines

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024

ISBN

9783031737589

303173758X

Edizione

[1st ed. 2024.]

Descrizione fisica

1 online resource (81 pages)

Collana

SpringerBriefs in Mathematics, , 2191-8201

Altri autori (Persone)

CainesPeter E

Disciplina

003.76

Soggetti

Stochastic processes

Stochastic Systems and Control

Processos estocàstics

Anàlisi estocàstica

Llibres electrònics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

Introduction -- Problem Statement -- Asymptotic Maximum Likelihood Identification -- Geometric Results -- Lagrangian Adaptation -- Proof of Theorem 5.2 -- Index.

Sommario/riassunto

This book introduces a cutting-edge continuous time stochastic linear quadratic (LQ) adaptive control algorithm for fully observed linear stochastic systems with unknown parameters. The adaptive estimation algorithm is engineered to drive the maximum likelihood estimate into the set of parameters representing the true closed-loop dynamics. By incorporating a performance monitoring feature, this approach ensures that the estimate converges to the true system parameters. Concurrently, it delivers optimal long-term LQ closed-loop performance. This groundbreaking work offers a significant advancement in the field of stochastic control systems.