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

Authors:
R. J. Kuo;M. C. Shieh;J. W. Zhang;K. Y. Chen
Affiliations:
-;-;-;-
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2013

Citing 15
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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.