Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.
You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.
After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.
Comprehensive introduction into Fuzzy Set Theory, Fuzzy Logic, and some areas of Computational Intelligence that are strongly related to Fuzzy Sets
The book is intended to cover most of the basic topics in Fuzzy Sets Theory and Fuzzy Logic from a mathematical point of view as well as most of the current applications of the presented theory and can be used as textbook at both undergraduate and graduate levels
Written by a leading expert in the field
This book presents a mathematically-based introduction into the fascinating topic of Fuzzy Sets and Fuzzy Logic and might be used as textbook at both undergraduate and graduate levels and also as reference guide for mathematician, scientists or engineers who would like to get an insight into Fuzzy Logic.
Fuzzy Sets have been introduced by Lotfi Zadeh in 1965 and since then, they have been used in many applications. As a consequence, there is a vast literature on the practical applications of fuzzy sets, while theory has a more modest coverage. The main purpose of the present book is to reduce this gap by providing a theoretical introduction into Fuzzy Sets based on Mathematical Analysis and Approximation Theory. Well-known applications, as for example fuzzy control, are also discussed in this book and placed on new ground, a theoretical foundation. Moreover, a few advanced chapters and several new results are included. These comprise, among others, a new systematic and constructive approach for fuzzy inference systems of Mamdani and Takagi-Sugeno types, that investigates their approximation capability by providing new error estimates.
Content Level »Research
Keywords »Artificial Neural Network - Fuzzy Control - Fuzzy Differential Equation - Fuzzy Inference System - Fuzzy Logic - Fuzzy Measure - Fuzzy Number - Fuzzy Set - Genetic Fuzzy System - Neuro-Fuzzy System