The application of an artificial immune system-based back-propagation neural network with feature selection to an RFID positioning system

Abstract

This study uses the Received Signal Strength Indication (RSSI) values of RFID to predict the position of picking staff for warehouse management. A proposed feature selection-based back-propagation (BP) neural network that uses an artificial immune system (AIS) (FSBP-AIS) to determine the connecting weights of a neural network learns the relationship between the RSSI values and the position of the picking staff. In addition, the proposed FSBP-AIS is able to determine the representative features, or inputs, during training. Once a picking staff's position is known, this information is used to plan the picking route for picking staff if a new order arrives. The computational results indicate that the proposed FSBP-AIS can provide better predictions than a traditional BP neural network, BP neural network with stepwise regression to determine the important inputs, and regression method.