Fuzzy Logic in Computational Intelligence : Applications and Case Studies
Language: English
Published by Wiley Apr 2027, 2027
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Add to basketItem description from seller
Neuware - 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.…
Seller Inventory # 9781394345663
- Title
- Fuzzy Logic in Computational Intelligence : Applications and Case Studies
- Author
- Rahul Kar
- Publisher
- Wiley Apr 2027
- Publication year
- 2027
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1394345666
- ISBN 13
- 9781394345663
- Item weight
- 930 grams
- Dimensions
- 229x152x30 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
Rahul Kar works in the School of Mathematics, Kalyani Mahavidyalaya, Nadia, West Bengal, India and is pursuing a Ph.D. in Mathematics from Bankura University. With eight years of teaching experience, he has published many research papers and edited 15 books. He is a member of the Indian Mathematical Society and an honorary member of the Carmels Research Institute, Dakar, Senegal.
Gunjan Mukherjee, PhD is an associate professor in the Department of Computational Sciences, Brainware University, Kolkata, India. He has published many papers in journals and international conferences of repute and several books, including a series of textbooks that he solely authored. His research interests include computer vision, machine learning, soft computing, and image processing.
Biswadip Basu Mallik, PhD is an Associate Professor of Mathematics in the Department of Basic Sciences and Humanities, the Institute of Engineering and Management, Kolkata, India. He has been involved in teaching and research for more than 21 years and has published several research papers and book chapters in various scientific journals, more than 15 books, and five patents. His fields of research focus on computational fluid dynamics and mathematical modeling.
Ashok Kumar Shaw, PhD is a Professor of Mathematics, Head of the Department of Mathematics and Basic Science and Humanities, and the Dean of Research and Development at the Budge Budge Institute of Technology, Kolkata, India. He has published about 30 papers in international journals of repute, and three books. His research focuses on applied mathematics, reliability optimization, maintenance, fuzzy mathematics, inventory and supply chain management.
"About the title" may belong to another edition of this title.
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