지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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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.
목차
Abstract
1. Introduction
2. Literature Review
3. Proposed RPD Procedure
4. Illustrative Example
5. Conclusions
References
참고문헌 (15)
참고문헌 신청
1. [학술지(정기간행물)] - Adeli, H / 2001 / Neural Networks in Civil Engineering： 1989~2000 / Computer-Aided Civil and Infrastructure Engineering 16 : 126 ~ 142
2. [학술지(정기간행물)] - Chang, H.-H / 2008 / A Data Mining Approach to Dynamic Multiple Responses in Taguchi Experimental Design / Expert Systems with Applications 35 (3) : 1095 ~ 1103
3. [학술지(정기간행물)] - Chang, H.-H / 2011 / Neuro-Genetic Approach to Optimize Parameter Design of Dynamic Multiresponse Experiments / Applied Soft Computing 11 : 436 ~ 442
4. [학술지(정기간행물)] - Koksoy, O / 2005 / A Hopfield Neural Network Approach to the Dual Response Problem / Quality and Reliability Engineering International 21 : 595 ~ 603
5. [학술지(정기간행물)] - Ma, H.-Y / 2010 / Applying Hierarchical Genetic Algorithm Based Neural Network and Multiple Objective Evolutionary Algorithm to Optimize Parameter Design with Dynamic Characteristics / Journal of Quality 17 (4) : 311 ~ 325
2. [학술지(정기간행물)] - Chang, H.-H / 2008 / A Data Mining Approach to Dynamic Multiple Responses in Taguchi Experimental Design / Expert Systems with Applications 35 (3) : 1095 ~ 1103
3. [학술지(정기간행물)] - Chang, H.-H / 2011 / Neuro-Genetic Approach to Optimize Parameter Design of Dynamic Multiresponse Experiments / Applied Soft Computing 11 : 436 ~ 442
4. [학술지(정기간행물)] - Koksoy, O / 2005 / A Hopfield Neural Network Approach to the Dual Response Problem / Quality and Reliability Engineering International 21 : 595 ~ 603
5. [학술지(정기간행물)] - Ma, H.-Y / 2010 / Applying Hierarchical Genetic Algorithm Based Neural Network and Multiple Objective Evolutionary Algorithm to Optimize Parameter Design with Dynamic Characteristics / Journal of Quality 17 (4) : 311 ~ 325
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UCI(KEPA) : I410-ECN-0101-2014-325-000831464