基于遗传算法和加权D-S信息融合的结构多损伤位置识别

将信息融合技术应用于结构的多损伤定位问题，为了解决证据融合理论中的不同证据应具有不同的重要性的问题，提出了一种基于遗传算法的加权平衡证据调整方法。该方法利用遗传算法来确定最优化的或者近似最优化的权重系数，然后依据加权平均值和优先权的证据分布形态对证据进行了加权调整，调整后的证据保证了加权平均值不变以及优先权证据分布形态的稳定。仿真结果表明，采用了信息融合方法的结构多损伤定位，可以产生比单一信息源更精确、更完全的估计和判决，而基于遗传算法的加权平衡证据调整方法具有更好的对多损伤定位的识别能力，优于基本D-S证据组合方法以及其他加权证据组合方法。

IDENTIFICATION OF STRUCTURAL MULTIPLE DAMAGED LOCATIONS BASED ON GENETIC ALGORITHMS AND DEMPSTER-SHAFER FUSION THEORY

Information fusion technology is applied to identify structural multiple damaged locations. It is considered that the multiple evidence from different information sources of different importance or reliability are not equally important when they are combined according to Dempster-Shafer theory, which is seldom considered in the existent combination methods. A new method, i.e. weighted evidence balance method, is presented to solve this problem. The method first searches for the optimal weighting coefficients of different evidences using genetic algorithms, then balances the considered evidences according to the weighted average of all and the preferred evidence, and finally combines them. Thus, It is guarantied that the balanced evidences won't change the weighted average of all and the preferred evidence. The simulation results demonstrate the excellent performance of the weighted evidence balance method to identify multiple damage locations as compared with other methods, such as those methods based on mode shape change or frequency change, basic evidence theory and other weighted evidence combination methods.