基于改进樽海鞘群和最小二乘支持向量机算法的新型弹药质量评估方法

doi:10.12382/bgxb.2021.0240

摘要/Abstract

摘要： 针对新型弹药产品质量评估样本数据少、试验消耗大、未有效利用制造过程质量数据等问题，提出一种基于改进樽海鞘群和最小二乘支持向量机（LSSVM）的新型弹药质量评估方法。以新型弹药靶试数据为输入，对批次弹药发射成功率进行贝叶斯估计。利用LSSVM建立弹药批次制造质量数据与弹药发射成功率之间关系的评估模型，使用精英质心和反向学习策略改进的樽海鞘群算法对LSSVM进行优化，有效提升评估模型的准确性，并以某新型弹药为例对评估模型有效性进行验证。验证结果表明：与传统LSSVM、粒子群优化的LSSVM及樽海鞘群优化的LSSVM模型相比，该模型具有较高的准确度和较强的鲁棒性，对新型弹药产品的质量评估有一定借鉴意义。

关键词: 新型弹药, 质量评估, 樽海鞘群算法, 最小二乘支持向量机, 贝叶斯方法

Abstract: For the problems about the less sample data，large test consumption and ineffective use of manufacturing process quality data, in order to solve these problems，an ammunition quality evaluation method is proposed based on least squares support vector machine(LSSVM)optimized by improved salp swarm algorithm.The firing success rate of batches of new ammunition is estimated based on Bayesian by using target test data as input.On this basis，an evaluation model of the relationship between ammunition batch manufacturing quality data and ammunition firing success rate is developed using LSSVM.The LSSVM is optimized with an elite center of mass and a salp swarm algorithm improved by backward learning strategy，which effectively improves the accuracy of the evaluation model.And the validity of the evaluation model was verified by using a new type of ammunition as an example.The validated results show that the model has higher accuracy and stronger robustnesscompared with the traditional LSSVM，LSSVM improved by particle swarm algorithm and LSSVM improved bysalp swarm algorithm.

Key words: ammunition, qualityevaluation, salpswarmalgorithm, leastsquaressupportvectormachine, Bayesianmethod

中图分类号:

TJ410

杨建新，兰小平，冯亚东，杨一铭，郭志明. 基于改进樽海鞘群和最小二乘支持向量机算法的新型弹药质量评估方法[J]. 兵工学报, 2022, 43(5): 1012-1022.

YANG Jianxin， LAN Xiaoping， FENG Yadong， YANG Yiming， GUO Zhiming. An Ammunition Quality Evaluation Method Based on Least Squares Support Vector Machine[J]. Acta Armamentarii, 2022, 43(5): 1012-1022.

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