基于PSO-BP神经网络的船舶生产设计软件成熟度评估方法
Maturity evaluation method of ship production design software based on PSO-BP neural network
王冲 1华德睿1
作者信息
- 1. 武汉理工大学 船海与能源动力工程学院,湖北 武汉 430063
- 折叠
摘要
[目的]针对现有船舶生产设计软件成熟度评估方法尚不明确、评估存在模糊性等问题,提出一种船舶生产设计软件成熟度评估模型.[方法]该模型根据船舶生产设计过程中船体、管系、舾装和涂装 4 个阶段,构建成熟度评估体系并确定各级成熟因子.结合贝叶斯网络与模糊最优最劣法,提出一种完全客观的赋权方法以提高数据集的准确性.引入粒子群优化(PSO)算法改进反向传播(BP)神经网络,通过PSO对BP神经网络的权值和阈值进行最优化,避免局部最优问题,并对软件的成熟度进行全面评估.[结果]实例分析表明,PSO-BP比BP评价的均方根误差减少了 56.86%.[结论]该模型的精度和速度较好,能够满足实际评估需求,为船舶工业软件成熟度评估提供一种新思路.
Abstract
[Objective]This paper proposes a new maturity assessment model for ship production design software in order to address the problem in which the existing methods are unclear and their assessment is am-biguous.[Methods]Based on the four stages of the ship production design process,namely hull,piping,outfitting and coating,a maturity assessment system is constructed and the maturity factors at each level de-termined.Combined with the Bayesian network(BN)and fuzzy best-worst method(FBWM),a completely ob-jective weighting method is proposed to improve the accuracy of the dataset.A particle swarm optimization(PSO)algorithm is introduced to improve the back propagation(BP)neural network.The PSO optimizes the weights and thresholds of the BP neural network to avoid the local minimum problem and comprehensively evaluate the maturity of the software.[Results]The results show that the root mean square error of PSO-BP is reduced by 56.86% compared to BP.[Conclusion]The accuracy and speed of the proposed model are good enough to meet practical needs,thereby providing a new approach to software maturity assessment in the shipbuilding industry.
关键词
船舶生产设计软件/软件能力成熟度模型/贝叶斯网络-模糊最优最劣法/PSO-BP神经网络Key words
ship production design software/capability maturity model for software(SW-CMM)/Bayesian network and fuzzy best-worst method/PSO-BP neural network引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据