Optimal filtering algorithms for fast learning in feedforward neural networks

[1]  Francesco Palmieri,et al.  Sound localization with a neural network trained with the multiple extended Kalman algorithm , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[2]  Lee A. Feldkamp,et al.  Decoupled extended Kalman filter training of feedforward layered networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[3]  Thomas P. Vogl,et al.  Rescaling of variables in back propagation learning , 1991, Neural Networks.

[4]  Francesco Palmieri,et al.  MEKA-a fast, local algorithm for training feedforward neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[5]  Sharad Singhal,et al.  Training feed-forward networks with the extended Kalman algorithm , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[6]  S. Citrin,et al.  Fast learning process of multi-layer neural nets using recursive least squares technique , 1989, International 1989 Joint Conference on Neural Networks.

[7]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[8]  Raymond L. Watrous Learning Algorithms for Connectionist Networks: Applied Gradient Methods of Nonlinear Optimization , 1988 .

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[11]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.