文献类型：期刊文献

中文题名：基于PCA-GA神经网络模式识别的炭纤维复合材料导电综合性能优化及预测的研究

英文题名：Optimization and prediction research on carbon fiber composite integrated conductive performance based on PCA-GANN pattern recognition

作者：杨榛[1,2];顾幸生[2];梁晓怿[1];张睿[1];凌立成[1]

机构：[1]华东理工大学化工学院,上海200237;[2]华东理工大学信息科学与工程学院,上海200237

年份：2008

卷号：25

期号：12

起止页码：1543

中文期刊名：计算机与应用化学

外文期刊名：Computers and Applied Chemistry

收录：CSTPCD;;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：国家自然科学基金(50672025);上海市科技登山计划(065258033;06JC1401)

语种：中文

中文关键词：主成分分析;GABP神经网络;模式识别;炭纤维复合材料导电综合性能

外文关键词：principal component analysis, GABP neural networks, pattern recognition, integrated conductive of carbon fiber composite

摘要：在炭纤维/ABS树脂基复合材料导电性和拉伸强度实验数据的基础上,利用主成分分析-遗传算法后向传播(Principal Component Analysis-Genetic Algorithm Back Propagation,PCA-GABP)神经网络模式识别法,对炭纤维复合材料导电综合性参数进行智能分析识别。其中主成分分析法作为前处理过程优化样本集的选择,GABP神经网络较好地克服了BP网络非线性映射易陷入局部极小值问题。给出性能优良的目标参数优化区,并用实验予以验证。该模式识别方法可减少实验工作量,提高工作效率,在复合材料设计领域具有理论应用前景。
On the basis of experimental data about carbon fiber/ABS resin matrix composites conductivity and tensile strength, PCA- GABP （Principal Component Analysis-Genetic Algorithm Back Propagation Principal） neural network method for pattern recognition was used for material design. To optimize the impact factors of sample sets, principal component analysis was used as a pre-treatment process. GABPNN could effectively overcome shortcoming of BPNN such as easily leading into local minimum. The method was used to recognize and analysis integrated conductive parameters of carbon fiber composites. Then, the optimized district of performance parameters was given. The predicted results agreed well with the experimental ones. Therefore, PCA-GABPNN pattern recognition prospect theoretical design method for composites design.