一种改进T-S模糊神经网络及其在装备保障力量动态部署中的应用  

An Improved T-S FNN and its Application in Dynamic Deployment of Equipment Support Unit

作　　者：陈晓山[1] 张勇明[1] 毛超 Chen Xiaoshan;Zhang Yongming;Mao Chao(Department of Operational Research & Planning, Naval University of Engineering, Wuhan 430033, China)

机构地区：[1]海军工程大学作战运筹与规划系

出　　处：《兵工自动化》2019年第7期56-59,共4页Ordnance Industry Automation

摘　　要：为提高模糊神经网络的全局搜索能力和学习效率,提出一种常规T-S 模糊神经网络(Takagi-Sugeno fuzzyneural network,T-S FNN)的改进方法。将遗传算法引入常规T-S FNN,采用其搜索功能来确定T-S FNN 的权值和参数,建立装备保障力量动态部署模型,对装备保障力量部署问题进行说明,通过改进方法训练学习,获取动态部署方案,并对其进行比较与分析。结果表明:该方法提高了自适应性和学习效率,可为装备保障力量动态部署的快速辅助决策提供参考。For improving global searching ability and learning efficiency of fuzzy neural network, put forward the Takagi-Sugeno fuzzy neural network (T-S FNN) improved method. It uses genetic algorithms (GA) to fix the weight and system parameter of T-S FNN with GA’s search function. The model of dynamic deployment of equipment support unit has been constituted and applied with the improved arithmetic, describe the equipment support unit deployment, then the project of dynamic deployment is achieved by training and learning. And carry out comparison and analysis. The results show that the method can enhances adaptability and learning efficiency of T-S FNN and can supply a quick-reference for the assistant decision of dynamic deployment of equipment support unit.

关 键 词：T-S模型 模糊神经网络 遗传算法 装备保障 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]