Design optimization with back-propagation neural networks
| العنوان: | Design optimization with back-propagation neural networks |
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| المؤلفون: | Shou-Jen Lee, Henzer Chen |
| المصدر: | Journal of Intelligent Manufacturing. 2:293-303 |
| بيانات النشر: | Springer Science and Business Media LLC, 1991. |
| سنة النشر: | 1991 |
| مصطلحات موضوعية: | Mathematical optimization, Artificial neural network, business.industry, Computer science, Computer Science::Neural and Evolutionary Computation, Feed forward, Function (mathematics), Industrial and Manufacturing Engineering, Nonlinear programming, Hopfield network, Function approximation, Design objective, Artificial Intelligence, Artificial intelligence, business, Design methods, Software |
| الوصف: | A methodology with back-propagation neural network models is developed to explore the artificial neural nets (ANN) technology in the new application territory of design optimization. This design methodology could go beyond the Hopfield network model, Hopfield and Tank (1985), for combinatorial optimization problems In this approach, pattern classification with back-propagation network, the most demonstrated power of neural networks applications, is utilized to identify the boundaries of the feasible and the infeasible design regions. These boundaries enclose the multi-dimensional space within which designs satisfy all design criteria. A feedforward network is then incorporated to perform function approximation of the design objective function. This approximation is performed by training the feedforward network with objective functions evaluated at selected design sets in the feasible design regions. Additional optimum design sets in the classified feasible regions are calculated and included in the successive training sets to improve the function mapping. Iteration is continued until convergent criteria are satisfied. This paper demonstrates that the artificial neural nets technology provides a global perspective of the entire design space with good and near optimal solutions. ANN can indeed be a potential technology for design optimization. |
| تدمد: | 1572-8145 0956-5515 |
| DOI: | 10.1007/bf01471177 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::ec580b83002a99f883f33763941289b3 https://doi.org/10.1007/bf01471177 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi...........ec580b83002a99f883f33763941289b3 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15728145 09565515 |
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| DOI: | 10.1007/bf01471177 |