Process modelling, and optimisation, using neural networks

Pao Yue-kong Library Electronic Theses Database

Process modelling, and optimisation, using neural networks

 

Author: Cheng, Kai-hing
Title: Process modelling, and optimisation, using neural networks
Degree: M.Sc.
Year: 1998
Subject: Plastics -- Molding
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Manufacturing Engineering
Pages: 92 leaves : ill. (some col.) ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1453589
URI: http://theses.lib.polyu.edu.hk/handle/200/4587
Abstract: Neural Network has a very strong ability to capture complex input-output relationships of complex functions under noisy environment make it a very good candidate to model Industrial Processes. In this paper, a training algorithm has been designed which used correlation coefficients, relative errors and absolute errors to measure the goodness of fit. The trained models were tested with testing data concurrently. In order to prove the effectiveness of the training algorithm, three known functions were trained without and with noise. Variables search techniques are used to find the significant factors. Conventional methods using fractional factorial designed, ANOVA, and regression Modelings were also used to compare the results. Lastly an optimization experiments to find out the optimum settings of the significant factors which confirmed with verification run to be very close to the actual. We concluded by suggesting ANN to be developed as the experimental analysis tools but requires to install faster speed computers as the training time is sometimes quite long.

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