A general backpropagation algorithm for feedforward neural networks learning
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.
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.
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.
ieeexplore.ieee.org
Showing the best result for this search. See all results
