Decoupled extended Kalman filter training of feedforward layered networks

Presents a training algorithm for feedforward layered networks based on a decoupled extended Kalman filter (DEKF). The authors present an artificial process noise extension to DEKF that increases its convergence rate and assists in the avoidance of local minima. Computationally efficient formulations for two particularly natural and useful cases of DEKF are given. Through a series of pattern classification and function approximation experiments, three members of DEKF are compared with one another and with standard backpropagation (SBP). These studies demonstrate that the judicious grouping of weights along with the use of artificial process noise in DEKF result in input-output mapping performance that is comparable to the global extended Kalman algorithm, and is often superior to SBP, while requiring significantly fewer presentations of training data than SBP and less overall training time than either of these procedures.>