{"title":"Jerry M Mendel","description":null,"products":[{"product_id":"uncertain-rule-based-fuzzy-logic-systems-book-jerry-m-mendel-9780130409690","title":"Uncertain Rule-Based Fuzzy Logic Systems","description":"This text explains the complete development of fuzzy logic systems and explores a new methodology to build better and more intelligent systems. It features two short primers on fuzzy sets and fuzzy logic and two case studies which illustrate and expand on the theories introduced.","brand":"WoB","offers":[{"title":"GB \/ VERY_GOOD \/ INTERNAL","offer_id":49655819731217,"sku":"GOR013257831","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":50344739307793,"sku":"CIN0130409693VG","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ GOOD \/ SBYB","offer_id":51693678330129,"sku":"CIN0130409693G","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/0130409693.jpg?v=1751290327"},{"product_id":"uncertain-rule-based-fuzzy-systems-book-jerry-m-mendel-9783319513690","title":"Uncertain Rule-Based Fuzzy Systems","description":"The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":49736576270609,"sku":"NGR9783319513690","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3319513699.jpg?v=1751252654"},{"product_id":"explainable-uncertain-rule-based-fuzzy-systems-book-jerry-m-mendel-9783031353802","title":"Explainable Uncertain Rule-Based Fuzzy Systems","description":"The third edition of this textbook presents a further updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications, from time-series forecasting to knowledge mining to classification to control and to explainable AI (XAI). This latest edition again begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty, leading to type-2 fuzzy sets and systems. New material is included about how to obtain fuzzy set word models that are needed for XAI, similarity of fuzzy sets, a quantitative methodology that lets one explain in a simple way why the different kinds of fuzzy systems have the potential for performance improvements over each other, and new parameterizations of membership functions that have the potential for achieving even greater performance for all kinds of fuzzy systems. For hands-on experience, the book provides information on accessing MATLAB, Java, and Python software to complement the content. 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Deconvolution is the unraveling of convolution. It is the inverse problem of generating the system's input from knowledge about the system's output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both effects. It draws upon ideas from Maximum Likelihood, when unknown parameters are random. It leads to linear and nonlinear signal processors that provide high-resolution estimates of a system's input. All aspects of MLD are described, from first principles in this book. The purpose of this volume is to explain MLD as simply as possible. To do this, the entire theory of MLD is presented in terms of a convolutional signal generating model and some relatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, namely the convolutional model. The book focuses on three aspects of MLD: (1) specification of a probability model for the system's measured output; (2) determination of an appropriate likelihood function; and (3) maximization of that likelihood function. Many practical algorithms are obtained. Computational aspects of MLD are described in great detail. Extensive simulations are provided, including real data applications.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52618152313105,"sku":"NLS9781461279853","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9781461279853.jpg?v=1761532346"}],"url":"https:\/\/www.worldofbooks.com\/en-gb\/collections\/author-books-by-jerry-m-mendel.oembed","provider":"World of Books ","version":"1.0","type":"link"}