Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2004.830801
Title: New dynamical optimal learning for linear multilayer FNN
Authors: Tan, K.C. 
Tang, H.J.
Keywords: Back propagation
Dynamical optimal learning (DOL)
Feedforward neural networks (FNN)
Stability
Issue Date: Nov-2004
Source: Tan, K.C., Tang, H.J. (2004-11). New dynamical optimal learning for linear multilayer FNN. IEEE Transactions on Neural Networks 15 (6) : 1562-1568. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2004.830801
Abstract: This letter presents a new dynamical optimal learning (DOL) algorithm for three-layer linear neural networks and investigates its generalization ability. The optimal learning rates can be fully determined during the training process. The mean squared error (mse) is guaranteed to be stably decreased and the learning is less sensitive to initial parameter settings. The simulation results illustrate that the proposed DOL algorithm gives better generalization performance and faster convergence as compared to standard error back propagation algorithm. © 2004 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/56795
ISSN: 10459227
DOI: 10.1109/TNN.2004.830801
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