The Prediction Method Based on Neural Network Algorithm for the Bearing Capacity of Launching Site

doi:10.12382/bgxb.2021.0245

Abstract

Abstract: A rapid prediction method of the bearing capacity of launching site is proposed to improve the quick response ability of the missile launching，which is based on linear multiple regression algorithm，back propagation(BP) algorithm and radial basis function (RBF) algorithm. An optimized Latin hypercube sampling method with parameter sensitivity is applied to construct the sample space. The approximate models evaluated by different algorithms under different loads are established and proved to be effective. Evaluation algorithm of the bearing capacity of unknown launching site is established to predict the probable maximum deflection under launch load using dynamical response of the launching site under erection load. The results show that the RBF algorithm has the best prediction performance and the regression coefficients under erection and launch loads are 0.941 and 0.983.The average error between predicted and simulated results is 10.46%. For the launching site with higher bearing capacity，the residual error of the evaluation algorithm ranges in ±2 mm.

Key words: launchingsite, predictionofthebearingcapacity, backpropagation, radialbasisfunction, linearmultipleregressionalgorithm

CLC Number:

TJ762.1

LI Mingjun， JIANG Yi， MA Liqi， PAN Xiao. The Prediction Method Based on Neural Network Algorithm for the Bearing Capacity of Launching Site[J]. Acta Armamentarii, 2022, 43(5): 982-991.

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第43卷第5期2022 年5月
兵工学报ACTA ARMAMENTARII
Vol.43No.5May2022

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