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http://dx.doi.org/10.7737/KMSR.2012.29.3.081

Robust Parameter Design Based on Back Propagation Neural Network  

Arungpadang, Tritiya R. (Department of Systems Management and Engineering, Pukyong National University)
Kim, Young Jin (Department of Systems Management and Engineering, Pukyong National University)
Publication Information
Korean Management Science Review / v.29, no.3, 2012 , pp. 81-89 More about this Journal
Abstract
Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.
Keywords
Robust Parameter Design; Dual Response Approach; Back-Propagation Neural Networks; Response Surface Methodology;
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