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Learning Accuracy from Limited Data: Using Mega-Fuzzification Method to Improve Small Data Set Learning Accuracy for Early Flexible Manufacturing System Scheduling - Softcover
Synopsis
Many machine learning methods to improve system scheduling have been proposed in the field of artificial intelligence. Most of them rely on a large amount of data having been gathered, and knowledge derived from the limited data available in the early manufacturing stages is usually too fragile for a flexible manufacturing system. This causes the accuracy of prediction with regard to the production strategy to be very low. It is therefore a challenging problem to increase the accuracy of predictions derived from early knowledge acquisition. This thesis is aimed at increasing the accuracy of machine learning for FMS scheduling using small data sets. Methodologies proposed include data continualized concept, mega-fuzzification, application of fuzzy theory, and data domain external expansion approach. Also, this thesis considers the data bias phenomenon that often occurs in small data sets and provides a method for its adjustment. Furthermore, a method is proposed to determine the domain external expansion magnitude when data range is unknown. Briefly, the results show that the proposed approaches can increase the learning accuracy in a broad range of applications.
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About the Author
Fengming M. Chang received his Ph.D. in Industrial and Information Management from National Cheng Kung University in Taiwan. He is now an Assistant Professor at Asia University. His research interests include small data set learning and reduction for Neuro-Fuzzy. Besides, his name is included in the upcoming 2009 edition Who''s Who in the World.
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Learning Accuracy from Limited Data : Using Mega-Fuzzification Method to Improve Small Data Set Learning Accuracy for Early Flexible Manufacturing System Scheduling
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many machine learning methods to improve system scheduling have been proposed in the field of artificial intelligence. Most of them rely on a large amount of data having been gathered, and knowledge derived from the limited data available in the early manufacturing stages is usually too fragile for a flexible manufacturing system. This causes the accuracy of prediction with regard to the production strategy to be very low. It is therefore a challenging problem to increase the accuracy of predictions derived from early knowledge acquisition. This thesis is aimed at increasing the accuracy of machine learning for FMS scheduling using small data sets. Methodologies proposed include data continualized concept, mega-fuzzification, application of fuzzy theory, and data domain external expansion approach. Also, this thesis considers the data bias phenomenon that often occurs in small data sets and provides a method for its adjustment. Furthermore, a method is proposed to determine the domain external expansion magnitude when data range is unknown. Briefly, the results show that the proposed approaches can increase the learning accuracy in a broad range of applications. Seller Inventory # 9783836486033
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Learning Accuracy from Limited Data | Using Mega-Fuzzification Method to Improve Small Data Set Learning Accuracy for Early Flexible Manufacturing System Scheduling
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Learning Accuracy from Limited Data | Using Mega-Fuzzification Method to Improve Small Data Set Learning Accuracy for Early Flexible Manufacturing System Scheduling | Fengming Chang | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783836486033 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 105588078