PSO-LSSVM-Based Ammunition Assembly Quality Prediction Method

doi:10.12382/bgxb.2021.0867

Abstract

Abstract: Based on particle swarm optimization (PSO)-least squares support vector machines (LSSVM), an ammunition assembly quality prediction method is proposed to address the problems of complex ammunition assembly process, existence of various influencing factors for the assembly process quality, and low assembly efficiency. Through gray entropy correlation analysis, key quality characteristics affecting the ammunition assembly quality are extracted and used as the input vectors of the prediction model to reduce the complexity and calculation workload. Using PSO-LSSVM as the modeling tool, the parameters of LSSVM are optimized by the PSO algorithm, and a prediction model is developed for ammunition assembly quality. Taking the prediction of runout during the docking assembly of a certain type of ammunition as an example, the model is compared with LSSVM model and Back-Propagation (BP) neural network prediction model. The experimental results demonstrate that the proposed PSO-LSSVM-based prediction method for ammunition assembly quality is feasible and effective, which can accurately predict the quality of ammunition assembly.

Key words: ammunitionassembly, qualityprediction, grayentropycorrelationanalysis, particleswarmoptimization, leastsquaressupportvectormachine

CLC Number:

TH164

QIU Jiarong, ZENG Pengfei, SHAO Weiping, ZHAO Lijun, HAO Yongping. PSO-LSSVM-Based Ammunition Assembly Quality Prediction Method[J]. Acta Armamentarii, 2022, 43(9): 2379-2387.

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