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Guided Neural Network Learning Using a Fuzzy Controller, with Applications to Textile Spinning
Author(s) -
Wu P.,
Fang SC.,
Nuttle H.L.W.,
Wilson J.R.,
King R.E.
Publication year - 1995
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/j.1475-3995.1995.tb00020.x
Subject(s) - computer science , artificial neural network , fuzzy logic , spinning , controller (irrigation) , relation (database) , key (lock) , textile , artificial intelligence , process (computing) , machine learning , control engineering , control theory (sociology) , data mining , control (management) , engineering , materials science , mechanical engineering , agronomy , composite material , biology , computer security , operating system
We apply neural networks to build a metamodel of the relation between key input parameters and output performance measures of a simulated textile spinning plant. We investigate two different neural network estimation algorithms, namely back‐propagation and an algorithm incorporating a fuzzy controller for the learning rate. According to our experience, both algorithms are capable of providing high‐quality predictions. In addition, results obtained using a fuzzy controller for the learning rate suggest a significant potential for speeding up the training process.