哈尔滨工程大学学报2012，Vol.33Issue(7)：806-810,5.DOI:10.3969/j.issn.1006-7043.201108006
改进PSO训练的BPNN方法的舰船主尺度建模
Modeling of the principal dimensions of large vessels based on a BPNN trained by an improved PSO
摘要
Abstract
A back propagation neural network (BPNN) trained by an improved particle swarm optimization (PSO) was applied to the principal dimensions of large vessels by regression analysis.First,the improved PSO learning factors concerning the iterative process were adjusted adaptively and the BPNN trained process was optimized by using an improved PSO.Secondly,a new BPNN method was applied to establish a mathematic model of an aircraft's principal dimensions (including overall length,breadth moulded,length of the design waterline,breadth of the design waterline,draft,and full load displacement).Finally,compared with the results of traditional polynomial regression,a BPNN trained by an improved PSO has higher accuracy and fine characteristics of smooth at every subsection.Therefore,the mathematic model has a guidance effect on the scheme demonstration and overall design of large vessels.关键词
舰船主尺度/回归分析/改进粒子群优化算法/BP神经网络Key words
large vessels principal dimensions/regression analysis/improved particle swarm optimization/back propagation neural network (BPNN)分类
交通工程引用本文复制引用
张海鹏,韩端锋,郭春雨..改进PSO训练的BPNN方法的舰船主尺度建模[J].哈尔滨工程大学学报,2012,33(7):806-810,5.基金项目
国家自然科学基金资助项目(61004008) （61004008）
中央高校基本科研业务费专项基金资助项目(HEUCF100105). （HEUCF100105）
