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Applications of Fuzzy Logic in Decision Making and Management Science

Edited by Subrata Jana, Edited by Biswadip Basu Mallik, Edited by Anirban Sarkar, Edited by Chiranjibe Jana

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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 theÂ book andÂ itsÂ contentÂ layout will be done so meticulously proving theÂ enoughÂ insightsÂ ofÂ theÂ Â subjects to the readers so thatÂ theÂ readersÂ can easily pursue their research concept from theÂ book. Overall the book serve as the purpose of repository of good amount of information and their technical presentations.

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