지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.
목차
1. 서론
2. 문헌 연구
3. 제안 모형
4. 수치 예제
5. 결론 및 토의
참고문헌
참고문헌 (23)
참고문헌 신청
2. [학술지(정기간행물)] - Chang, H. H / 2005 / Applications of Neural Networks and Genetic Algorithms to Taguchi’s Robust Design / International Journal of Electronic Business Management 3 (2) : 90 ~ 96
3. [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
4. [학술지(정기간행물)] - Chang, H. H / 2011 / Neuro-Genetic Approach to Optimize Parameter Design of Dynamic Multiresponse Expreiments / Applied Soft Computing 11 : 436 ~ 442
5. [학술지(정기간행물)] - Cook, D. F / 2000 / Combining a Neural Network with a Genetic Algorithm for Process Parameter Optimization / Engineering Application of Artificial Intelligence 13 (4) : 391 ~ 396
6. [학술지(정기간행물)] - Das, P / 2010 / Hybridization of Artificial Neural Network using Desirability Functions for Process Optimization / International Journal for Quality Research 4 (1) : 37 ~ 50
3. [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
4. [학술지(정기간행물)] - Chang, H. H / 2011 / Neuro-Genetic Approach to Optimize Parameter Design of Dynamic Multiresponse Expreiments / Applied Soft Computing 11 : 436 ~ 442
5. [학술지(정기간행물)] - Cook, D. F / 2000 / Combining a Neural Network with a Genetic Algorithm for Process Parameter Optimization / Engineering Application of Artificial Intelligence 13 (4) : 391 ~ 396
6. [학술지(정기간행물)] - Das, P / 2010 / Hybridization of Artificial Neural Network using Desirability Functions for Process Optimization / International Journal for Quality Research 4 (1) : 37 ~ 50
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UCI(KEPA) : I410-ECN-0101-2014-500-002816811