{"product_id":"9789401060400","title":"International Series in Intelligent Technologies","description":"\u003ch1\u003eInternational Series in Intelligent Technologies\u003c\/h1\u003e \u003ch2\u003eBabuška, Robert\u003c\/h2\u003e \u003cp\u003eRule-based fuzzy modeling has been recognised as a powerful  technique for the modeling of partly-known nonlinear systems. Fuzzy  models can effectively integrate information from different sources,  such as physical laws, empirical models, measurements and heuristics.  Application areas of fuzzy models include prediction, decision  support, system analysis, control design, etc. \u003cem\u003eFuzzy Modeling  for\u003c\/em\u003e \u003cem\u003eControl\u003c\/em\u003e addresses fuzzy modeling from the systems and  control engineering points of view. It focuses on the selection of  appropriate model structures, on the acquisition of dynamic fuzzy  models from process measurements (fuzzy identification), and on the  design of nonlinear controllers based on fuzzy models. \u003cbr\u003e  To automatically generate fuzzy models from measurements, a  comprehensive methodology is developed which employs fuzzy clustering  techniques to partition the available data into subsets characterized  by locally linear behaviour. The relationships between the presented  identification method and linear regression are exploited, allowing  for the combination of fuzzy logic techniques with standard system  identification tools. Attention is paid to the trade-off between the  accuracy and transparency of the obtained fuzzy models. Control design  based on a fuzzy model of a nonlinear dynamic process is addressed,  using the concepts of model-based predictive control and internal  model control with an inverted fuzzy model. To this end, methods to  exactly invert specific types of fuzzy models are presented. In the  context of predictive control, branch-and-bound optimization is  applied. \u003cbr\u003e  The main features of the presented techniques are illustrated by means  of simple examples. In addition, three real-world applications are  described. Finally, software tools for building fuzzy models from  measurements are available from the author.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2012-10-26\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9789401060400\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-94-011-4868-9\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 260\u003c\/p\u003e ","brand":"Springer Netherlands","offers":[{"title":"Default Title","offer_id":44359167180940,"sku":"9789401060400","price":197.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9789401060400.jpg?v=1775735615","url":"https:\/\/lateknightbooks.com\/products\/9789401060400","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}