{"product_id":"identification-of-nonlinear-systems-using-andrzej-janczak-9783540231851","title":"Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach","description":"\u003cp\u003eThis monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. \"Identification of Nonlinear Systems Using Neural Networks and Polynomal Models\" is useful for researchers, engineers and graduate students in nonlinear systems and neural network theory.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Andrzej Janczak\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 3540231854\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9783540231851\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Springer\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 11\/18\/2004\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 199\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.15lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 11.00h x 8.50w x 0.46d","brand":"Andrzej Janczak","offers":[{"title":"Paperback","offer_id":48519792230655,"sku":"9783540231851","price":109.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_39a3474a-af24-463e-aca6-b4e7dcdcec5a.jpg?v=1778738841","url":"https:\/\/www.whiterainbookhouse.com\/products\/identification-of-nonlinear-systems-using-andrzej-janczak-9783540231851","provider":"WR Book House","version":"1.0","type":"link"}