LEADER 04630nam 22007215 450 001 9910972125703321 005 20250818100547.0 010 $a9783642758942 010 $a3642758940 024 7 $a10.1007/978-3-642-75894-2 035 $a(CKB)3400000000107627 035 $a(SSID)ssj0001295695 035 $a(PQKBManifestationID)11766080 035 $a(PQKBTitleCode)TC0001295695 035 $a(PQKBWorkID)11343897 035 $a(PQKB)11483405 035 $a(DE-He213)978-3-642-75894-2 035 $a(MiAaPQ)EBC3092433 035 $a(PPN)238008304 035 $a(EXLCZ)993400000000107627 100 $a20121227d1990 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdaptive Algorithms and Stochastic Approximations /$fby Albert Benveniste, Michel Metivier, Pierre Priouret 205 $a1st ed. 1990. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1990. 215 $a1 online resource (XII, 364 p.) 225 1 $aStochastic Modelling and Applied Probability,$x2197-439X ;$v22 300 $a"With 24 figures." 311 08$a9783540528944 311 08$a3540528946 311 08$a9783642758966 311 08$a3642758967 320 $aIncludes bibliographical references and index. 327 $aI. Adaptive Algorithms: Applications -- 1. General Adaptive Algorithm Form -- 2. Convergence: the ODE Method -- 3. Rate of Convergence -- 4. Tracking Non-Stationary Parameters -- 5. Sequential Detection; Model Validation -- 6. Appendices to Part I -- II. Stochastic Approximations: Theory -- 1. O.D.E. and Convergence A.S. for an Algorithm with Locally Bounded Moments -- 2. Application to the Examples of Part I -- 3. Analysis of the Algorithm in the General Case -- 4. Gaussian Approximations to the Algorithms -- 5. Appendix to Part II: A Simple Theorem in the ?Robbins-Monro? Case -- Subject Index to Part I -- Subject Index to Part II. 330 $aAdaptive systems are widely encountered in many applications ranging through adaptive filtering and more generally adaptive signal processing, systems identification and adaptive control, to pattern recognition and machine intelligence: adaptation is now recognised as keystone of "intelligence" within computerised systems. These diverse areas echo the classes of models which conveniently describe each corresponding system. Thus although there can hardly be a "general theory of adaptive systems" encompassing both the modelling task and the design of the adaptation procedure, nevertheless, these diverse issues have a major common component: namely the use of adaptive algorithms, also known as stochastic approximations in the mathematical statistics literature, that is to say the adaptation procedure (once all modelling problems have been resolved). The juxtaposition of these two expressions in the title reflects the ambition of the authors to produce a reference work, both for engineers who use these adaptive algorithms and for probabilists or statisticians who would like to study stochastic approximations in terms of problems arising from real applications. Hence the book is organised in two parts, the first one user-oriented, and the second providing the mathematical foundations to support the practice described in the first part. The book covers the topcis of convergence, convergence rate, permanent adaptation and tracking, change detection, and is illustrated by various realistic applications originating from these areas of applications. 410 0$aStochastic Modelling and Applied Probability,$x2197-439X ;$v22 606 $aProbabilities 606 $aChemometrics 606 $aComputational intelligence 606 $aProbability Theory 606 $aMathematical Applications in Chemistry 606 $aComputational Intelligence 615 0$aProbabilities. 615 0$aChemometrics. 615 0$aComputational intelligence. 615 14$aProbability Theory. 615 24$aMathematical Applications in Chemistry. 615 24$aComputational Intelligence. 676 $a519.2 700 $aBenveniste$b Albert$4aut$4http://id.loc.gov/vocabulary/relators/aut$059526 702 $aMe?tivier$b Michel$f1931-$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPriouret$b P$g(Pierre),$f1939-$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910972125703321 996 $aAdaptive Algorithms and Stochastic Approximations$94430160 997 $aUNINA