In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtaine
Abstract-The Back-propagation (BP) training algorithm is a renowned representative of all iterative ...
The traditional Back-propagation Neural Network (BPNN) Algorithm is widely used in solving many real...
An efficient technique namely Backpropagation training with adaptive parameters using Lyapunov Stabi...
The back propagation algorithm has been successfully applied to wide range of practical problems. Si...
Abstract — The back propagation algorithm has been successfully applied to wide range of practical p...
Back Propagation (BP) is commonly used algorithm that optimize the performance of network for traini...
Artificial Neural Network (ANN) can be trained using back propagation (BP). It is the most widely us...
The back propagation algorithm is one of the popular learning algorithms to train self learning feed...
A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks...
Abstract—Back propagation is one of the well known training algorithms for multilayer perceptron. Ho...
The back-propagation algorithm calculates the weight changes of an artificial neural network, and a ...
This paper presents some simple techniques to improve the backpropagation algorithm. Since learning ...
A convergence analysis for learning algorithms based on gradient optimization methods was made and a...
The Back Propagation algorithm or its variation on Multilayered Feedforward Networks is widely used...
The new modifications of multilayered neurak networks training algorithms in a generalized training ...
Abstract-The Back-propagation (BP) training algorithm is a renowned representative of all iterative ...
The traditional Back-propagation Neural Network (BPNN) Algorithm is widely used in solving many real...
An efficient technique namely Backpropagation training with adaptive parameters using Lyapunov Stabi...
The back propagation algorithm has been successfully applied to wide range of practical problems. Si...
Abstract — The back propagation algorithm has been successfully applied to wide range of practical p...
Back Propagation (BP) is commonly used algorithm that optimize the performance of network for traini...
Artificial Neural Network (ANN) can be trained using back propagation (BP). It is the most widely us...
The back propagation algorithm is one of the popular learning algorithms to train self learning feed...
A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks...
Abstract—Back propagation is one of the well known training algorithms for multilayer perceptron. Ho...
The back-propagation algorithm calculates the weight changes of an artificial neural network, and a ...
This paper presents some simple techniques to improve the backpropagation algorithm. Since learning ...
A convergence analysis for learning algorithms based on gradient optimization methods was made and a...
The Back Propagation algorithm or its variation on Multilayered Feedforward Networks is widely used...
The new modifications of multilayered neurak networks training algorithms in a generalized training ...
Abstract-The Back-propagation (BP) training algorithm is a renowned representative of all iterative ...
The traditional Back-propagation Neural Network (BPNN) Algorithm is widely used in solving many real...
An efficient technique namely Backpropagation training with adaptive parameters using Lyapunov Stabi...
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