Demand Feature Identifying for Emergency Goods under Earthquake Rescue Logistics by Vague Set Based BP Neural Network

Abstract

During the preliminary stage of rescue for earthquake disaster, some important information needs to be provided by identifying the key demand features such as quantity, necessary degree and urgent degree for emergency goods. To dispose uncertain information, especially unknown information for identifying above features, vague set is introduced into the conventional BP neural network. The relation among supportive degree, counteractive degree and unknown degree are analyzed. For disposing unknown information for vague set, the method for transforming vague set information into fuzzy set information is proposed. A series of rules for transforming vague values into fuzzy values are presented. Hereby, the fuzzy membership degree function is given. The structure of four-layer multi output VBP neural network is designed, and its implement steps are studied. To validate the validity of VBP neural network, it is applied to identify demand feature for emergency goods under earthquake. The result by VBP neural network is compared with those by conventional BP neural network and the VBP neural network which is based on conventional fuzzy membership degree transforming formula. The result shows that, the test precision by above VBP neural network is higher than those by the other two methods. As a novel learning method, VBP neural network is more suitable for training samples with uncertain information.