A recursive algorithm based on the extended Kalman filter for the training of feedforward neural models

The extended Kalman filter (EKF) is a well-known tool for the recursive parameter estimation of static and dynamic nonlinear models. In particular, the EKF has been applied to the estimation of the weights of feedforward and recurrent neural network models, i.e. to their training, and shown to be more efficient than recursive and nonrecursive first-order training algorithms; nevertheless, these first applications to the training of neural networks did not fully exploit the potentials of the EKF. In this paper, we analyze the specific influence of the EKF parameters for modeling problems, and propose a variant of this algorithm for the training of feedforward neural models which proves to be very efficient as compared to nonrecursive second-order algorithms. We test the proposed EKF algorithm on several static and dynamic modeling problems, some of them being benchmark problems, and which bring out the properties of the proposed algorithm.