基于量子遗传算法优化Hopfield神经网络军事训练效果评价

Optimization of Military Training Effect Evaluation of Hopfield Neural Network Based on Quantum Genetic Algorithm

新兵入伍训练是提升战斗力的基础,对新兵入伍训练成绩进行科学评价,将入伍训练19个指标作为评估因子,选取8个班平均训练成绩进行模糊聚类分析,按照得分情况制定了4个等级的划分方法,提出了一种基于量子遗传算法优化离散型Hopfield神经网络的评估模型,然后将待分级的4名士兵训练成绩进行Hopfield编码评估,并用实例对模型进行验证,为基层部队强化智能化军事训练方法手段提供了理论依据。

Recruit training is the basis of improving combat effectiveness.For scientific evaluation for recruit training scores,19 indexes of recruit training are selected as evaluation factors,average training scores of 8 classes are selected for fuzzy clustering analysis,and 4-layers classification method is made according to scores.Put forward the optimized discrete Hopfield neural network evaluation model based on quantum genetic algorithm.Then,4 soldiers’training scores are used for Hopfield coding evaluation,and the model is verified by example.It provides theoretical basis for enforcing basic unit intellectualization training method.