Maturity Evaluation Method of Ship Production Design Software Based on PSO-BP Neural Network

In view of the unclear maturity assessment methods and ambiguity in assessment of existing ship production design software, a maturity assessment model for ship production design software is proposed. Based on the four stages of ship production design process, including hull, piping, outfitting and coating, the maturity assessment system is constructed and the maturity factors at each level are determined. Combined with Bayesian network and fuzzy best worst method, a completely objective weighting method is proposed to improve the accuracy of dataset. 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 local minimum problem, and comprehensively evaluates the maturity of the software. The case shows the root mean square error of PSO-BP is reduced by 56.86% compared to BP, and the model accuracy and speed is good enough to meet practical needs. Key words：ship production design software; software maturity; Bayesian fuzzy best worst; PSO-BP neural network