Classification and Recognition Fusion Algorithm Based on Fuzzy Set Theory and Similarity Measure
Zekun Yao, Xin Zhang · 2021
D-S evidence theory is an imprecise information reasoning decision method most suitable for the application of target recognition, and it is widely used in uncertain information reasoning systems. The data fusion algorithm is one of the key technologies in the recognition and detection system. The data fusion algorithm based on fuzzy set theory and similarity measurement merges multiple classification recognition results, which helps to improve the accuracy of the detection and recognition system, and improve the detection and detection. Identify the reliability and practicability of the system. Use fuzzy set theory to generate reliability distribution function, measure the amount of information of multiple evidence, based on the evidence similarity measurement, revise the source evidence, and constitute the evidence. After conducting experiments on the fusion algorithm for the measured noise classification data of ships, the simulation results show that the fusion algorithm can further improve the trust in the classification and recognition results of the target. The fusion algorithm is feasible and effective, which greatly improves the performance of the original detection and recognition system.