{"product_id":"nonlinear-filters-9781118835814","title":"Nonlinear Filters","description":"\u003cb\u003eNONLINEAR FILTERS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eDiscover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eNonlinear Filters: Theory and Applications \u003c\/i\u003edelivers an insightful view on state and parameter estimation by merging ideas from control theory, statistical signal processing, and machine learning. Taking an algorithmic approach, the book covers both classic and machine learning-based filtering algorithms.\u003c\/p\u003e \u003cp\u003eReaders of \u003ci\u003eNonlinear Filters\u003c\/i\u003e will greatly benefit from the wide spectrum of presented topics including stability, robustness, computability, and algorithmic sufficiency. Readers will also enjoy: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eOrganization that allows the book to act as a stand-alone, self-contained reference\u003c\/li\u003e \u003cli\u003eA thorough exploration of the notion of observability, nonlinear observers, and the theory of optimal nonlinear filtering that bridges the gap between different science and engineering disciplines\u003c\/li\u003e \u003cli\u003eA profound account of Bayesian filters including Kalman filter and its variants as well as particle filter\u003c\/li\u003e \u003cli\u003eA rigorous derivation of the smooth variable structure filter as a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values\u003c\/li\u003e \u003cli\u003eA concise tutorial on deep learning and reinforcement learning\u003c\/li\u003e \u003cli\u003eA detailed presentation of the expectation maximization algorithm and its machine learning-based variants, used for joint state and parameter estimation\u003c\/li\u003e \u003cli\u003eGuidelines for constructing nonparametric Bayesian models from parametric ones\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for researchers, professors, and graduate students in engineering, computer science, applied mathematics, and artificial intelligence, \u003ci\u003eNonlinear Filters: Theory and Applications\u003c\/i\u003e will also earn a place in the libraries of those studying or practicing in fields involving pandemic diseases, cybersecurity, information fusion, augmented reality, autonomous driving, urban traffic network, navigation and tracking, robotics, power systems, hybrid technologies, and finance.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Peyman Setoodeh\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Wiley\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 03\/10\/2022\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 304\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.24lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.69d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781118835814","brand":"Wiley","offers":[{"title":"Hardcover","offer_id":40689457102963,"sku":"9.78112E+12","price":179.95,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/products\/img_7e3a3f89-8e6b-4045-80fd-a95673ff8c98.jpg?v=1675265139","url":"https:\/\/bookstorenmore.com\/en-de\/products\/nonlinear-filters-9781118835814","provider":"Bookstore N More","version":"1.0","type":"link"}