Add to ORKGNeural networks have been used for modelling the nonlinear characteristics of memoryless nonlinear channels using the backpropagation learning (BP) with experimental training data. The mean transient and convergence behavior of a simplified two-layer neural network has been studied in [2]. The network was trained with zero mean Gaussian data. This paper extends these results to include the effects of the weight fluctuations upon the mean-square-error (MSE). A new methodology is presented which can be ex
The back propagation algorithm is one of the popular learning algorithms to train self learning feed...
In this study, we focus on feed-forward neural networks with a single hidden layer. The research tou...
An adaptive back-propagation algorithm parameterized by an inverse temperature 1/T is studied and co...
Neural networks have been used for modelling the nonlinear characteristics of memoryless nonlinear c...
Analysis of a normalised backpropagation (NBP) algorithm employed in feed-forward multilayer nonline...
Error backpropagation in feedforward neural network models is a pop-ular learning algorithm that has...
Error backpropagation in feedforward neural network models is a popular learning algorithm that has ...
Summary form only given, as follows. A novel learning algorithm for multilayered neural networks is ...
A backpropagation learning algorithm for feedforward neural networks with an adaptive learning rate ...
A backpropagation learning algorithm for feedforward neural networks with an adaptive learning rate ...
Since the presentation of the backpropagation algorithm, a vast variety of improvements of the techn...
Backpropagation is a supervised learning algorithm for training multi-layer neural networks for func...
This paper presents a mathematical analysis of the occurrence of temporary minima during training of...
Abstract—This paper introduces a general framework for de-scribing dynamic neural networks—the layer...
A Neural Network is a powerful data modeling tool that is able to capture and represent complex inpu...
The back propagation algorithm is one of the popular learning algorithms to train self learning feed...
In this study, we focus on feed-forward neural networks with a single hidden layer. The research tou...
An adaptive back-propagation algorithm parameterized by an inverse temperature 1/T is studied and co...
Neural networks have been used for modelling the nonlinear characteristics of memoryless nonlinear c...
Analysis of a normalised backpropagation (NBP) algorithm employed in feed-forward multilayer nonline...
Error backpropagation in feedforward neural network models is a pop-ular learning algorithm that has...
Error backpropagation in feedforward neural network models is a popular learning algorithm that has ...
Summary form only given, as follows. A novel learning algorithm for multilayered neural networks is ...
A backpropagation learning algorithm for feedforward neural networks with an adaptive learning rate ...
A backpropagation learning algorithm for feedforward neural networks with an adaptive learning rate ...
Since the presentation of the backpropagation algorithm, a vast variety of improvements of the techn...
Backpropagation is a supervised learning algorithm for training multi-layer neural networks for func...
This paper presents a mathematical analysis of the occurrence of temporary minima during training of...
Abstract—This paper introduces a general framework for de-scribing dynamic neural networks—the layer...
A Neural Network is a powerful data modeling tool that is able to capture and represent complex inpu...
The back propagation algorithm is one of the popular learning algorithms to train self learning feed...
In this study, we focus on feed-forward neural networks with a single hidden layer. The research tou...
An adaptive back-propagation algorithm parameterized by an inverse temperature 1/T is studied and co...