Intelligent RFID positioning system through immune-based feed-forward neural network

This study intends to propose a feed-forward neural network for RFID positioning system. The proposed network integrates artificial immune network for optimization (Opt-aiNET) and artificial immune system (AIS) with clone selection to train the connecting weights of feed-forward neural network. It is able to learn the relationship between the received signal strength indication and picking cart position. Since the proposed learning algorithm owns both the merits of Opt-aiNET and AIS with clone selection, it is able to avoid falling into the local optimum and possesses the learning capability. The computational results for learning two continuous functions show that the proposed algorithm has better performance than other immune-based back-propagation neural network. In addition, the model evaluation results also indicate that the proposed algorithm really can predict the picking cart position more correctly than other methods.