About the book
Unlock the power to navigate real-world uncertainty with this comprehensive guide, blending foundational theory with practical case studies to show you exactly how to integrate fuzzy logic and machine learning for unmatched predictive accuracy.
Machine learning has emerged as a transformative force in modern research, and the integration of fuzzy logic with machine learning techniques has shown significant promise in improving accuracy and precision. Fuzzy logic is based on the principles of uncertainty and has applications across a wide range of predictive and probabilistic environments. From agriculture to healthcare and numerous other sectors, fuzzy logic can be utilized to develop innovative and effective solutions. This book examines the pivotal role of fuzzy logic in enhancing computational systems’ ability to handle uncertainty and imprecision. The book provides a thorough introduction to fuzzy logic, explaining its fundamental concepts and theories while demonstrating its capacity to mimic human reasoning to address vagueness and ambiguity. It explores practical applications in decision-making, pattern recognition, and control systems, with examples such as Nissan’s anti-lock brakes, Honda’s auto engines, and Mitsubishi Electric’s elevator controls. Beyond artificial intelligence and machine learning, fuzzy logic finds applications in fields like evolutionary computing, computer vision, and image processing, enabling advancements in classifier optimization and fine-tuning. By integrating theoretical foundations with practical applications and future directions, the book serves as an essential resource for understanding and leveraging fuzzy logic in computational intelligence and across diverse interdisciplinary domains.
The reader will find the volume:
- Explores the core principles of fuzzy logic and its integration into computational intelligence, providing a strong theoretical foundation for researchers and practitioners;
- Features diverse case studies demonstrating the practical implementation of fuzzy logic in fields such as healthcare, robotics, finance, and engineering;
- Showcases how fuzzy systems enhance decision-making in uncertain and complex environments, offering tools to tackle real-world challenges;
- Written to cater to both beginners seeking an introduction and experts aiming to deepen their knowledge in fuzzy logic systems.
Audience
Researchers, academics, graduate students, engineers, data scientists, and AI technologists in computer science, artificial intelligence, and applied mathematics seeking advanced insights into fuzzy logic systems and their applications.
Editions of Fuzzy Logic in Computational Intelligence
- Hardcover
- ISBN 9781394345663
- EBook
- ISBN 9781394345694
- EBook
- ISBN 9781394345687
- EBook
- ISBN 9781394345670
Read an Excerpt
Unlock the power to navigate real-world uncertainty with this comprehensive guide, blending foundational theory with practical case studies to show you exactly how to integrate fuzzy logic and machine learning for unmatched predictive accuracy.
Machine learning has emerged as a transformative force in modern research, and the integration of fuzzy logic with machine learning techniques has shown significant promise in improving accuracy and precision. Fuzzy logic is based on the principles of uncertainty and has applications across a wide range of predictive and probabilistic environments. From agriculture to healthcare and numerous other sectors, fuzzy logic can be utilized to develop innovative and effective solutions. This book examines the pivotal role of fuzzy logic in enhancing computational systems’ ability to handle uncertainty and imprecision. The book provides a thorough introduction to fuzzy logic, explaining its fundamental concepts and theories while demonstrating its capacity to mimic human reasoning to address vagueness and ambiguity. It explores practical applications in decision-making, pattern recognition, and control systems, with examples such as Nissan’s anti-lock brakes, Honda’s auto engines, and Mitsubishi Electric’s elevator controls. Beyond artificial intelligence and machine learning, fuzzy logic finds applications in fields like evolutionary computing, computer vision, and image processing, enabling advancements in classifier optimization and fine-tuning. By integrating theoretical foundations with practical applications and future directions, the book serves as an essential resource for understanding and leveraging fuzzy logic in computational intelligence and across diverse interdisciplinary domains.
The reader will find the volume:
- Explores the core principles of fuzzy logic and its integration into computational intelligence, providing a strong theoretical foundation for researchers and practitioners;
- Features diverse case studies demonstrating the practical implementation of fuzzy logic in fields such as healthcare, robotics, finance, and engineering;
- Showcases how fuzzy systems enhance decision-making in uncertain and complex environments, offering tools to tackle real-world challenges;
- Written to cater to both beginners seeking an introduction and experts aiming to deepen their knowledge in fuzzy logic systems.
Audience
Researchers, academics, graduate students, engineers, data scientists, and AI technologists in computer science, artificial intelligence, and applied mathematics seeking advanced insights into fuzzy logic systems and their applications.
Frequently Asked Questions
What is Fuzzy Logic in Computational Intelligence about?
Unlock the power to navigate real-world uncertainty with this comprehensive guide, blending foundational theory with practical case studies to show you exactly how to integrate fuzzy logic and machine learning for unmatched predictive accuracy.
Machine learning has emerged as a transformative force in modern research, and the integration of fuzzy logic with machine learning techniques has shown significant promise in improving accuracy and precision. Fuzzy logic is based on the principles of uncertainty and has applications across a wide range of predictive and probabilistic environments. From agriculture to healthcare and numerous other sectors, fuzzy logic can be utilized to develop innovative and effective solutions. This book examines the pivotal role of fuzzy logic in enhancing computational systems’ ability to handle uncertainty and imprecision. The book provides a thorough introduction to fuzzy logic, explaining its fundamental concepts and theories while demonstrating its capacity to mimic human reasoning to address vagueness and ambiguity. It explores practical applications in decision-making, pattern recognition, and control systems, with examples such as Nissan’s anti-lock brakes, Honda’s auto engines, and Mitsubishi Electric’s elevator controls. Beyond artificial intelligence and machine learning, fuzzy logic finds applications in fields like evolutionary computing, computer vision, and image processing, enabling advancements in classifier optimization and fine-tuning. By integrating theoretical foundations with practical applications and future directions, the book serves as an essential resource for understanding and leveraging fuzzy logic in computational intelligence and across diverse interdisciplinary domains.
The reader will find the volume:
- Explores the core principles of fuzzy logic and its integration into computational intelligence, providing a strong theoretical foundation for researchers and practitioners;
- Features diverse case studies demonstrating the practical implementation of fuzzy logic in fields such as healthcare, robotics, finance, and engineering;
- Showcases how fuzzy systems enhance decision-making in uncertain and complex environments, offering tools to tackle real-world challenges;
- Written to cater to both beginners seeking an introduction and experts aiming to deepen their knowledge in fuzzy logic systems.
Audience
Researchers, academics, graduate students, engineers, data scientists, and AI technologists in computer science, artificial intelligence, and applied mathematics seeking advanced insights into fuzzy logic systems and their applications.
What core themes, tropes, or subjects are explored in Fuzzy Logic in Computational Intelligence?
Computing and Information Technology > Computer science
Where can I read a sample of Fuzzy Logic in Computational Intelligence?
You can read an official preview of the few pages here https://www.book2look.com/book/9781394345663
Is Fuzzy Logic in Computational Intelligence part of a series, and can it be read as a standalone?
Yes it is a part of series Advances in Learning Analytics for Intelligent Cloud-IoT Systems
Who is/are the Author/s of the book Fuzzy Logic in Computational Intelligence?
John Wiley & Sons
Who is the Publisher of the book Fuzzy Logic in Computational Intelligence?
Wiley
What are the ISBN numbers for the physical and digital editions?
Fuzzy Logic in Computational Intelligence is available as hardcover(ISBN 9781394345663) and ebook(ISBN 9781394345694) and ebook(ISBN 9781394345687) and ebook(ISBN 9781394345670)
Where can I buy Fuzzy Logic in Computational Intelligence online or support local independent bookshops?
You can buy Fuzzy Logic in Computational Intelligence from below
Where can I buy Fuzzy Logic in Computational Intelligence?