Structure identification of fuzzy model
Fuzzy Sets and Systems
Learning decision tree classifiers
ACM Computing Surveys (CSUR)
A review of machine learning in dynamic scheduling of flexible manufacturing systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Approximate modeling for high order non-linear functions using small sample sets
Expert Systems with Applications: An International Journal
A parametric fuzzy logic approach to dynamic part routing under full routing flexibility
Computers and Industrial Engineering
Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems
Expert Systems with Applications: An International Journal
Data attribute reduction using binary conversion
WSEAS Transactions on Computers
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Provided with plenty of data (experience), data mining techniques are widely used to extract suitable management skills from the data. Nevertheless, in the early stages of a manufacturing system, only rare data can be obtained, and built scheduling knowledge is usually fragile. Using small data sets, this research's purpose is improving the accuracy of machine learning for flexible manufacturing system (FMS) scheduling. The study develops a data trend estimation technique and combines it with mega-fuzzification and adaptive-network-based fuzzy inference systems (ANFIS). The results of the simulated FMS scheduling problem indicate that learning accuracy can be significantly improved using the proposed method involving a very small data set.