• Title: Fuzzy Mathematics
• Author(s) Etienne E Kerre, John Mordeson
• Publisher: Mdpi AG (November 21, 2018); eBook (Creative Commons Licensed)
• License(s): CC BY-NC-ND 4.0
• Hardcover: 286 pages
• eBook: PDF
• Language: English
• ISBN-10/ASIN: 303897322X
• ISBN-13: 978-303897322
• Share This:  

Book Description

This book provides a timely overview of topics in fuzzy mathematics. It lays the foundation for further research and applications in a broad range of areas. It contains break-through analysis on how results from the many variations and extensions of fuzzy set theory can be obtained from known results of traditional fuzzy set theory.

The book contains not only theoretical results, but a wide range of applications in areas such as decision analysis, optimal allocation in possibilistics and mixed models, pattern classification, credibility measures, algorithms for modeling uncertain data, and numerical methods for solving fuzzy linear systems.

• N/A

• Amazon

• Applied Mathematics
• Artificial Intelligence, Machine Learning, and Logic Programming
• Operations Research (OR) and Optimization
• Bioinformatics, Computational Biology, and Healthcare IT

• Fuzzy Mathematics (Etienne E Kerre, et al)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - Read Online

• Theory and Applications of Ordered Fuzzy Numbers

  This book offers comprehensive coverage on Ordered Fuzzy Numbers, providing readers with both the basic information and the necessary expertise to use them in a variety of real-world applications.

• Foundations of Fuzzy Logic and Semantic Web Languages

  This book provides a rigorous and succinct account of the mathematical methods and tools used for representing and reasoning with fuzzy information within Semantic Web languages. The book focuses on the three main streams of Semantic Web languages.

• Fuzzy Logic - Controls, Concepts, Theories and Applications

  This book introduces new concepts and theories of Fuzzy Logic Control for the application and development of robotics and intelligent machines - how it will play a significant role in the development of intelligent robotic, machine, and mechatronics systems.

• Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al)

  This book aims to provide an introduction to the topic of deep learning algorithms, coverss essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures and algorithms.

• Mathematics for Machine Learning (Marc P. Deisenroth, et al.)

  This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It provides a beautiful exposition of the mathematics underpinning modern machine learning.

• Mathematics for CS and Machine Learning (Jean Gallier, et al.)

  Covering everything you need to know about machine learning, now you can master the mathematics, computer science and statistics behind this field and develop your very own neural networks!

• Mathematical Methods in Quantum Mechanics (Gerald Teschl)

  This book is a brief, but self-contained, introduction to the mathematical methods of quantum mechanics, with a view towards applications to Schrodinger operators. Very clear and detailed way and supplements it by numerous exercises.

• Mathematical Foundations of Quantum Theory

  This is a collection of lecture notes, all shared a common interest in answering quantum issues. The goal is to give a mathematically clear and self-containing explanation of the main concepts of the modern language of quantum theory.

• The Functional Analysis of Quantum Information Theory

  This book provides readers with a concise introduction to current studies on operator-algebras and their generalizations, operator spaces and operator systems, with a special focus on their application in Quantum Information Science.

• Mathematics for Game Developers (Denny Burzynski)

  This book explores the mathematics behind how scenes appear, characters move, and action evolves. The basic ideas of how mathematical instruments control action and motion in the games you develop.

• Probability in Electrical Engineering and Computer Science

  This textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). Python labs enable the readers to experiment and consolidate their understanding.