混合神经网络匹配响应面的多学科设计方法  

Multi-disciplinary design method for the matched response surface based on the hybrid neural network

作　　者：刘道华[1] 王淑礼[1] 薛瑞[1] 

机构地区：[1]信阳师范学院计算机与信息技术学院,河南信阳464000

出　　处：《西安电子科技大学学报》2012年第5期161-167,共7页Journal of Xidian University

基　　金：河南省自然科学基金资助项目(122300410310;122300410151);河南省高校青年骨干教师计划资助项目(2009GGJS-075);河南省高等教育教学改革研究省级资助项目(2012SJGLX205)

摘　　要：为提高多学科设计方法的求解性能,构建了混合神经网络匹配响应面的多学科优化设计方法.将BP网络以及ART网络的优点相结合,为充分利用学科级优化时获得的目标结果样本自适应地改变传统固有的响应面结构,从而提高了响应面的精度,减少了学科级优化迭代的次数,进而提高了整个多学科优化方法的求解效率.通过具体的优化实例验证了该方法,以获得优化解的精度、优化解的平均迭代次数以及整个迭代占用的时间作对比,结果表明混合神经网络匹配响应面的多学科设计方法既能提高优化解的精度,又能提高算法的求解效率。In order to improve the solving performance of Multi-disciplinary design optimization （MDO）, a multidisciplinary design method for the matched response surface based on the hybrid neural network is proposed. By combining the advantages of the back-propagation （BP） network with the adaptive resonance theory （ART） network and making full use of the target results sample through discipline-level optimization to adaptively change the traditional response surface structure, the proposed method improves the accuracy of the response surface and reduces the number of times of iteration of the discipline-level optimization, which leads to a better solving efficiency for the multi-disciplinary optimization methods. The optimization method is validated by a specific example. Comparison studies in terms of solving accuracy, discipline optimization times, average number of times of iteration and iterative occupancy hours of＇ obtaining the optimization solution indicate that the method for the multi-disciplinary design of the matched response surface based on the hybrid neural network has a few advantages such as higher solution accuracy and higher computational efficiency.

关 键 词：BP网络 ART网络 多学科设计 响应面 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]