Roles of learning rates, artificial process noise and square root filtering for extended Kalman filter training

Singhal and Wu (1989) introduced the extended Kalman filter (EKF) neural network training algorithm. Since then, many modifications, simplifications and improvements of the EKF procedure have been developed and applied to difficult problems in pattern classification, signal processing and control. Recently, a number of enhancements related to learning rates and square root filtering for EKF training have been proposed, and performance superior to previously reported methods has been claimed. This paper serves to clarify issues related to the proper treatment of learning rates, artificial process noise and square root filtering for EKF training