基于Vague集BP网的应急救援物资需求模式识别
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摘要
为了解决应急救援物资需求模式识别中的不确定信息、特别是未知度信息的处理问题,将Vague集引入经典的BP网。研究Vague集信息转化为Fuzzy集信息的方法,给出Vague值转化为Fuzzy值遵循的一系列准则。据此,提出确定隶属度函数的方法;设计四层多输出的基于Vague集BP(VBP)网的拓扑结构;给出VBP网的具体实现步骤。实验表明,与经典BP网、基于经典模糊化公式的VBP网相比,采用所提出的VBP网,具有较高的测试精度。
To deal with the uncertain information,especially the unknown degree information processing in demand pattern identification for emergency rescue materials,we introduce Vague set into conventional BP neural network.We study the approach of transferring Vague set information to Fuzzy set information,give a series of criteria for transferring the Vague values to Fuzzy values.Based on these,we present the method of determining the fuzzy membership function,design the topology structure of Vague set-based BP(VBP) neural network(VBPNN) with four-layer multiple outputs,and give the specific implementation steps of VBPNN.The experiment shows,compared with conventional BP neural network and the VBPNN based on conventional fuzzification formula,using VBPNN proposed in the paper has higher test precision.
To deal with the uncertain information,especially the unknown degree information processing in demand pattern identification for emergency rescue materials,we introduce Vague set into conventional BP neural network.We study the approach of transferring Vague set information to Fuzzy set information,give a series of criteria for transferring the Vague values to Fuzzy values.Based on these,we present the method of determining the fuzzy membership function,design the topology structure of Vague set-based BP(VBP) neural network(VBPNN) with four-layer multiple outputs,and give the specific implementation steps of VBPNN.The experiment shows,compared with conventional BP neural network and the VBPNN based on conventional fuzzification formula,using VBPNN proposed in the paper has higher test precision.
引文
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[12]石玉强,王鸿绪.关于Vague值转化为Fuzzy集方法的分析[J].计算机工程与应用,2006,42(4):48-50.
[2]杨帆,郑宝柱,剡亮亮.基于BP神经网络的地震伤亡人数评估体系研究[J].灾防御技术,2009,4(4):428-435.
[3]于山,王海霞,马亚杰.三层BP神经网络地震灾害人员伤亡预测模型[J].地震工程与工程震动,2005,25(6):113-117.
[4]Zadeh L A.Fuzzy sets[J].Inform and Control,1965(8):338-356.
[5]Ralfstermark.A hybrid genetic fuzzy neural network algorithm de-signed for classification problems involving several groups[J].FuzzySets and Systems,2000,114(2):311-324.
[6]Hisao Ishibuchi,Manabu Nii.Numerical analysis of the learning offuzzified neural networks from fuzzy if–then rules[J].Fuzzy Setsand Systems,2001,120(2):281-307.
[7]Aliev R A,Guirimov B G,Bijan Fazlollahi,et al.Evolutionary algo-rithm-based learning of fuzzy neural networks.Part 2:Recurrent fuzzyneural networks[J].Fuzzy Sets and Systems,2009,160(17):2553-2566.
[8]Gau Wenlung,Buehrer D J.Vague sets[J].IEEE Transaction on Sys-tem,Man and Cyberbetics,1993,23(2):610-614.
[9]Chen S M.Measures of similarity between vague sets[J].Fuzzy Setsand Systems,1995,74(2):217-223.
[10]Boran F E,Kurt M,Akay D.A multicriteria intuitionistic fuzzy groupdecision making for selection of supplier with TOPSIS method[J].Ex-pert Systems with Applications,2009,36(8),11363-11368.
[11]石玉强,王鸿绪.Vague值向Fuzzy隶属度转化方法的准则[J].计算机工程与应用,2005,41(24):169-171,187.
[12]石玉强,王鸿绪.关于Vague值转化为Fuzzy集方法的分析[J].计算机工程与应用,2006,42(4):48-50.
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