Title :
A general backpropagation algorithm for feedforward neural networks learning
Author :
Yu, Xinghuo ; Efe, M. Onder ; Kaynak, Okyay
Author_Institution :
Fac. of Informatics & Commun., Central Queensland Univ., Rockhampton, Qld., Australia
fDate :
1/1/2002 12:00:00 AM
Abstract :
A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to rigorously analyze the convergence of weights, with the use of the algorithm, toward minima of the error function. Sufficient conditions to guarantee the convergence of weights for time varying inputs are derived. It is shown that most commonly used backpropagation learning algorithms are special cases of the developed general algorithm
Keywords :
backpropagation; convergence; feedforward neural nets; time-varying systems; Lyapunov function approach; commonly used backpropagation learning algorithms; error function minima; feedforward neural network learning; general backpropagation algorithm; sufficient conditions; time varying inputs; Algorithm design and analysis; Backpropagation algorithms; Convergence; Delay effects; Feedforward neural networks; Hopfield neural networks; Lyapunov method; Network address translation; Neural networks; Stability;
Journal_Title :
Neural Networks, IEEE Transactions on
