Improved clonal selection algorithm optimizing Neural Network for solving terminal anti-missile collaborative intercepting assistant decision-making model  

作　　者：Jinke Xiao Weimin Li Xinrong Xiao 

机构地区：[1]Air And Missile Defense College Air Force Engineering University Xi’an 710051,P.R.China [2]School of Light Industry And Food Sciences South China University Of Technology Guangzhou 510640,P.R.China

出　　处：《International Journal of Modeling, Simulation, and Scientific Computing》2017年第3期210-226,共17页建模、仿真和科学计算国际期刊（英文）

基　　金：the following Foundation Items:the National Natural Science Foundation of China(No.61102109,61473309 and 61472443);the 2014 Annual Aviation Science Funds(No.20140196003 and 20141996018).

摘　　要：Programming terminal high-low collaborative intercepting strategy scientifically and constructing assistant decision-making model with self-determination and intellectualization is onekey problem to enhance operational efficiency.Assistant decision-making model has been constructed after analysis on collaborative intercepting principle;then Improved Clonal Selection Algorithm Optimizing Neural Network(ICLONALGNN)is designed to solve the terminal anti-missile collaborative intercepting assistant decision-making model through introducing crossover operator to increase population diversity,introducing modified combination operator to make use of the information before crossover and mutation,introducing population update operator into traditional CLONALG to optimize Neural Network parameters.Experimental simulation confirms the superiority and practicability of the assistant decision-making model solved by ICLONALG-NN.

关 键 词：Terminal anti-missile system collaborative intercepting assistant decisionmaking clonal selection algorithm neural network 

分 类 号：O17[理学—数学]