applications fuzzy logic decision (27 resultados)

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Softcover. Condición: New. Estado de la sobrecubierta: no dj. First. Nonlinear decision models are increasingly being used in a wide range of applications, from finance and economics to engineering and computer science. Fuzzy Logic Applications in Nonlinear Decision Models provides a comprehensive exploration of the key applications and implications of fuzzy logic in nonlinear decision models, including the use of fuzzy sets and fuzzy rules for decision-making, the handling of uncertainty and ambiguity, and the enhancement of model accuracy and robustness. By examining the latest research and case studies, this book offers a detailed understanding of the opportunities and challenges presented by fuzzy logic in nonlinear decision models, and provides practical guidance for researchers and practitioners seeking to leverage these techniques to improve decision-making and problem-solving. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Hardcover. Condición: Very Good. B00Ks: Very Good/, 1987 Ilustrador. B00Ks: Very Good/, $274.53 O84936297O ANALYSIS 0F FUZZY INF0RMATI0N; CRC; = Vol. I, II, III, = V0LUME I: MATHEMATICS and L0GIC; O849362962; = VOLUME II: ARTIFICIAL INTELLIGENCE and DECISION SYSTEMS; O84936297O;= V0LUME III: APPLICATIONS in ENGINEERING and SCIENCE O849362989; Three B0Ks. BEZDEK, James C. Ph.D. CRC 1986 and 1987 3 H/C's. Blue Spine With Title In Polished White Letters, Hard Cover B00Ks: Very Good, Shelf, Edge And Corner Wear. Pages Printed On Off White Paper, In Fine/ Condition, That Appear To Be Lightly Read, And Are Clean, And Tight To The Spine, Shelf, Edge And Corner Wear. Covers Show Shelf Wear Rubbing. D/J: None. Description Applies To This BooK, ONLY. This BooK Is Hard To Find, Will Be Packaged And Shipped Carefully, To Avoid Shipping Damage And Will Make It, An Excellent Addition To Your Own Personal Library Collection, Or As A Gift. Might Require Extra Shipping And Handling To Your Destination, Due To Large Size And Weight. WORLD WIDE Shipping, AVAILABLE.

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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The fuzzy logic theory is a branch of mathematics dealing with uncertainty in measurement of any quantity or any estimation. The concept of fuzzy logic uses membership functions. The range of values from various functions or operations determines their construction. A defined rules set can create an application process and membership controls. Fuzzy applications include control system engineering, image processing, power engineering, industrial automation, robotics, consumer electronics and AI. Artificial intelligence, machine learning and expert systems have various applications that address complicated issues. The fuzzy logic inference rules have solved many problems in manufacturing and other industries. Auto engines by Honda, lift control by Mitsubishi Electric, palmtop computers by Hitachi, dishwashers by Matsushita and anti-lock brakes by Nissan are examples of corporations using machine-learning techniques with fuzzy principles. Fuzzy approaches and rule sets interpret computer vision, machine learning and evolution. Fuzzy sets can govern decision rules. Several areas use fuzzy systems in different ways. Computer vision, image processing and meta heuristic evolutionary computing are typical face research applications. Fuzzy theories can optimise and fine-tune the classifier model. Fuzzy theory is used in management, stock market analysis, information retrieval, linguistics, and behavioural science with good results. Fuzzy applications are seen in data mining and stock market prediction. The fuzzy machine learning model in the ensemble pattern accurately classifies and predicts all kinds of tasks. Fuzzy theories help maintain high accuracy. For categorisation and prediction, the ensemble pattern uses fuzzy concepts. The constant growth of fuzzy domain leads to several categorisation and prediction methods. Fuzzy type 2 and intuitionistic fuzzy logic exhibit promise accuracy and versatility. Such fuzzy logic variations can readily overcome the drawbacks of the simple fuzzy model.The book has been developed keeping in view about readers of different categories starting from the students to the professionals and researchers as well. The development of the book and its content layout will be done so meticulously proving the enough insights of the subjects to the readers so that the readers can easily pursue their research concept from the book. Overall the book serve as the purpose of repository of good amount of information and their technical presentations.

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Softcover. Condición: New. Estado de la sobrecubierta: no dj. First. Fuzzy logic is revolutionizing the way we make decisions, and 'Fuzzy Logic and Its Applications in Decision-Making' is at the forefront of this movement. This book explores the latest advancements in fuzzy logic, providing a comprehensive guide to harnessing the power of fuzzy logic to make data-backed decisions. From understanding the basics of fuzzy logic to implementing advanced analytics and visualization techniques, this book covers it all. With a focus on practical applications and real-world examples, this book is an essential resource for business leaders, marketers, and anyone looking to take their business to the next level. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condición: New. Estado de la sobrecubierta: no dj. First. Fuzzy Logic and Its Applications in Decision-Making presents a comprehensive exploration of the principles and applications of fuzzy logic in decision-making. This book provides a detailed examination of the methodologies and techniques used to apply fuzzy logic in a variety of decision-making contexts, enabling readers to develop a deeper understanding of the underlying mathematical principles. By applying the concepts presented, readers can enhance their ability to navigate complex decision-making environments and make informed choices in the face of uncertainty. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The fuzzy logic theory is a branch of mathematics dealing with uncertainty in measurement of any quantity or any estimation. The concept of fuzzy logic uses membership functions. The range of values from various functions or operations determines their construction. A defined rules set can create an application process and membership controls. Fuzzy applications include control system engineering, image processing, power engineering, industrial automation, robotics, consumer electronics and AI. Artificial intelligence, machine learning and expert systems have various applications that address complicated issues. The fuzzy logic inference rules have solved many problems in manufacturing and other industries. Auto engines by Honda, lift control by Mitsubishi Electric, palmtop computers by Hitachi, dishwashers by Matsushita and anti-lock brakes by Nissan are examples of corporations using machine-learning techniques with fuzzy principles. Fuzzy approaches and rule sets interpret computer vision, machine learning and evolution. Fuzzy sets can govern decision rules. Several areas use fuzzy systems in different ways. Computer vision, image processing and meta heuristic evolutionary computing are typical face research applications. Fuzzy theories can optimise and fine-tune the classifier model. Fuzzy theory is used in management, stock market analysis, information retrieval, linguistics, and behavioural science with good results. Fuzzy applications are seen in data mining and stock market prediction. The fuzzy machine learning model in the ensemble pattern accurately classifies and predicts all kinds of tasks. Fuzzy theories help maintain high accuracy. For categorisation and prediction, the ensemble pattern uses fuzzy concepts. The constant growth of fuzzy domain leads to several categorisation and prediction methods. Fuzzy type 2 and intuitionistic fuzzy logic exhibit promise accuracy and versatility. Such fuzzy logic variations can readily overcome the drawbacks of the simple fuzzy model.The book has been developed keeping in view about readers of different categories starting from the students to the professionals and researchers as well. The development of the book and its content layout will be done so meticulously proving the enough insights of the subjects to the readers so that the readers can easily pursue their research concept from the book. Overall the book serve as the purpose of repository of good amount of information and their technical presentations. 520 pp. Englisch.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The fuzzy logic theory is a branch of mathematics dealing with uncertainty in measurement of any quantity or any estimation. The concept of fuzzy logic uses membership functions. The range of values from various functions or operations determines their construction. A defined rules set can create an application process and membership controls. Fuzzy applications include control system engineering, image processing, power engineering, industrial automation, robotics, consumer electronics and AI. Artificial intelligence, machine learning and expert systems have various applications that address complicated issues. The fuzzy logic inference rules have solved many problems in manufacturing and other industries. Auto engines by Honda, lift control by Mitsubishi Electric, palmtop computers by Hitachi, dishwashers by Matsushita and anti-lock brakes by Nissan are examples of corporations using machine-learning techniques with fuzzy principles. Fuzzy approaches and rule sets interpret computer vision, machine learning and evolution. Fuzzy sets can govern decision rules. Several areas use fuzzy systems in different ways. Computer vision, image processing and meta heuristic evolutionary computing are typical face research applications. Fuzzy theories can optimise and fine-tune the classifier model. Fuzzy theory is used in management, stock market analysis, information retrieval, linguistics, and behavioural science with good results. Fuzzy applications are seen in data mining and stock market prediction. The fuzzy machine learning model in the ensemble pattern accurately classifies and predicts all kinds of tasks. Fuzzy theories help maintain high accuracy. For categorisation and prediction, the ensemble pattern uses fuzzy concepts. The constant growth of fuzzy domain leads to several categorisation and prediction methods. Fuzzy type 2 and intuitionistic fuzzy logic exhibit promise accuracy and versatility. Such fuzzy logic variations can readily overcome the drawbacks of the simple fuzzy model.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 520 pp. Englisch.

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