Learning Accuracy from Limited Data

Name: Learning Accuracy from Limited Data
Brand: VDM Verlag Dr. Mueller E.K.
SKU: 9783836486033
Price: 205.13 PLN
Availability: InStock

Using Mega-Fuzzification Method to Improve Small Data Set Learning Accuracy for Early Flexible Manufacturing System Scheduling

Autor: Fengming Chang

Język:

Oprawa: Miękka

Wydawca: VDM Verlag Dr. Mueller E.K.

Dostępność: Na zamówienie

Wysyłamy za 17-27 dni

205.13 zł

Many machine learning methods to improve system scheduling have been proposed in the field of artifi...

Informacje o książce

Autor

Fengming Chang

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2008

strony

EAN

9783836486033

ISBN

3836486032

Enbook ID

06846563

Wydawca

VDM Verlag Dr. Mueller E.K.

Waga

118

Wymiary

152 x 229 x 4

Pełny opis

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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Learning Accuracy from Limited Data

Using Mega-Fuzzification Method to Improve Small Data Set Learning Accuracy for Early Flexible Manufacturing System Scheduling

Informacje o książce

Pełny opis

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