Maturity evaluation method of ship production design software based on PSO-BP neural network
[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.
ship production design softwarecapability maturity model for software(SW-CMM)Bayesian network and fuzzy best-worst methodPSO-BP neural network
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