Applications Fuzzy Logic Decision (29 results)

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Hardcover. Condition: Very Good. B00Ks: Very Good/, 1987 (illustrator). 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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Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Condition: New. Provides in-depth explanations of picture fuzzy logic and its application to computational modeling problemsHelps readers understand the difference between various fuzzy logic methodsProvides concepts used to develop and solve prob.

Buch. Condition: 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.

Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Buch. Condition: 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. Condition: Neu. Applications of Fuzzy Logic in Decision Making and Management Science | Subrata Jana (u. a.) | Buch | Information Systems Engineering and Management | vii | Englisch | 2025 | Springer | EAN 9783031777189 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Buch. Condition: 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.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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 520 pp. Englisch.

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