RFID technology with anti-collision algorithm based on hybrid neural network

The development of intelligent warehousing for electric power materials requires an efficient and accurate material identification system. Current research is focused on optimizing RFID technology for tagging and identifying materials in the electric power industry. However, due to the large volume of materials and frequent "multi-tag collisions," existing identification capabilities are struggling to keep up with the rapid development of smart warehousing. To solve this issue, we have created a hybrid neural network-based anti-collision algorithm for RFID, specifically designed for the DFSA anti-collision protocol. The algorithm first predicts the number of tags using a hybrid neural network model. Then, based on the predicted tag count and using tag prefix information, we group and estimate the number of tags in each subset. Finally, we dynamically adjust the frame length for DFSA according to the tag count in each subset, improving the accuracy of RFID technology. Simulation results show that our proposed algorithm outperforms other algorithms in terms of system throughput, the ratio of total time slots consumed to collision time slots, and overall time slot consumption, effectively enhancing RFID identification accuracy.