LEADER 02045nam 2200649Ia 450 001 9910460254303321 005 20200520144314.0 010 $a1-282-86633-8 010 $a9786612866333 010 $a0-7735-7586-3 035 $a(CKB)2670000000079175 035 $a(OCoLC)759157086 035 $a(CaPaEBR)ebrary10424177 035 $a(SSID)ssj0000433953 035 $a(PQKBManifestationID)11328239 035 $a(PQKBTitleCode)TC0000433953 035 $a(PQKBWorkID)10410936 035 $a(PQKB)11174584 035 $a(MiAaPQ)EBC3332094 035 $a(CEL)432807 035 $a(CaBNvSL)slc00225494 035 $a(MiAaPQ)EBC3271215 035 $a(Au-PeEL)EBL3332094 035 $a(CaPaEBR)ebr10559044 035 $a(CaONFJC)MIL286633 035 $a(OCoLC)923234892 035 $a(EXLCZ)992670000000079175 100 $a20060223d2005 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBuilding Jewish roots$b[electronic resource] $ethe Israel experience /$fFaydra Shapiro 210 $aMontreal $cMcGill-Queen's University Press$dc2005 215 $a1 online resource (238 p.) 311 $a0-7735-3065-7 320 $aIncludes bibliographical references (p. [211]-219) and index. 327 $aLife at Livnot -- Routes to Israel -- Routes to other Jews -- Routes to Judaism -- Life after Livnot -- The power to choose. 606 $aIsrael and the diaspora 606 $aJewish youth$xEducation$zIsrael 606 $aJews$zCanada$xIdentity 606 $aJews$zUnited States$xIdentity 608 $aElectronic books. 615 0$aIsrael and the diaspora. 615 0$aJewish youth$xEducation 615 0$aJews$xIdentity. 615 0$aJews$xIdentity. 676 $a305.242089/924073 700 $aShapiro$b Faydra$f1970-$0793353 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460254303321 996 $aBuilding Jewish roots$91779330 997 $aUNINA LEADER 04071nam 2200481 450 001 9910427687103321 005 20220719130155.0 010 $a3-030-47439-9 024 7 $a10.1007/978-3-030-47439-3 035 $a(CKB)4100000011435794 035 $a(DE-He213)978-3-030-47439-3 035 $a(MiAaPQ)EBC6347278 035 $a(PPN)252511514 035 $a(EXLCZ)994100000011435794 100 $a20210209d2020 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNonlinear system identification $efrom classical approaches to neural networks, fuzzy models, and Gaussian processes /$fOliver Nelles 205 $aSecond edition. 210 1$aCham, Switzerland :$cSpringer,$d[2020] 210 4$d©2020 215 $a1 online resource (XXVIII, 1225 p. 670 illus., 179 illus. in color.) 311 $a3-030-47438-0 327 $aIntroduction -- Part One Optimization -- Introduction to Optimization -- Linear Optimization -- Nonlinear Local Optimization -- Nonlinear Global Optimization -- Unsupervised Learning Techniques -- Model Complexity Optimization -- Summary of Part 1 -- Part Two Static Models -- Introduction to Static Models -- Linear, Polynomial, and Look-Up Table Models -- Neural Networks -- Fuzzy and Neuro-Fuzzy Models -- Local Linear Neuro-Fuzzy Models: Fundamentals -- Local Linear Neuro-Fuzzy Models: Advanced Aspects -- Input Selection for Local Model Approaches -- Gaussian Process Models (GPMs) -- Summary of Part Two -- Part Three Dynamic Models -- Linear Dynamic System Identification -- Nonlinear Dynamic System Identification -- Classical Polynomial Approaches.-Dynamic Neural and Fuzzy Models -- Dynamic Local Linear Neuro-Fuzzy Models -- Neural Networks with Internal Dynamics -- Part Five Applications -- Applications of Static Models -- Applications of Dynamic Models -- Design of Experiments -- Input Selection Applications -- Applications of Advanced Methods -- LMN Toolbox -- Vectors and Matrices -- Statistics -- Reference -- Index. 330 $aThis book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential difficulties that may arise in practice. Moreover, the book is self-contained, requiring only a basic grasp of matrix algebra, signals and systems, and statistics. Accordingly, it can also serve as an introduction to linear system identification, and provides a practical overview of the major optimization methods used in engineering. The focus is on gaining an intuitive understanding of the subject and the practical application of the techniques discussed. The book is not written in a theorem/proof style; instead, the mathematics is kept to a minimum, and the ideas covered are illustrated with numerous figures, examples, and real-world applications. In the past, nonlinear system identification was a field characterized by a variety of ad-hoc approaches, each applicable only to a very limited class of systems. With the advent of neural networks, fuzzy models, Gaussian process models, and modern structure optimization techniques, a much broader class of systems can now be handled. 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