LEADER 04069nam 2200481 450 001 996418438803316 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$dİ2020 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. Although one major aspect of nonlinear systems is that virtually every one is unique, tools have since been developed that allow each approach to be applied to a wide variety of systems. . 606 $aSystem identification 606 $aNonlinear systems 606 $aAutomatic control engineering 615 0$aSystem identification. 615 0$aNonlinear systems. 615 0$aAutomatic control engineering. 676 $a003 700 $aNelles$b Oliver$f1969-$044047 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418438803316 996 $aNonlinear system identification$9357507 997 $aUNISA LEADER 01476nam 2200385 a 450 001 9910699729703321 005 20110103105834.0 035 $a(CKB)5470000002405652 035 $a(OCoLC)694741620 035 $a(EXLCZ)995470000002405652 100 $a20110103d2010 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aTraining novice drivers to shorten distraction time$b[electronic resource] 210 1$a[Washington, DC] :$c[U.S. Dept. of Transportation, National Highway Traffic Safety Administration],$d[2010] 215 $a1 online resource (2 unnumbered pages) $cillustrations 225 1 $aTraffic safety facts.$aTraffic tech.$aTechnology transfer series ;$v387 300 $aTitle from PDF caption title screen (nhtsa.gov, viewed Dec. 16, 2010). 300 $a"April 2010." 606 $aDistracted driving$zUnited States 606 $aDistracted driving$zUnited States$xPrevention 606 $aTeenage automobile drivers$xTraining of$zUnited States 615 0$aDistracted driving 615 0$aDistracted driving$xPrevention. 615 0$aTeenage automobile drivers$xTraining of 712 02$aUnited States.$bNational Highway Traffic Safety Administration. 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910699729703321 996 $aTraining novice drivers to shorten distraction time$93115439 997 $aUNINA