Add to ORKGBack Propagation (BP) was introduced by Rumelhart in 1986 [1]. BP is used for learning of Multi-Layer Percep-tron (MLP) and the error is propagating backward in the network. MLP after BP learning is known to be possible t
The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of imp...
Back-Propagation (BP)[Rumelhart et al, 1986] is a popular algorithm employed for training multilayer...
The error backpropagation learning algorithm (BP) is generally considered biologically implausible b...
Back Propagation (BP) was introduced by Rumelhart in 1986 [1]. BP is used for learning algorithm of ...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
Abstract — In the area of artificial neural networks, the Back Propagation (BP) learning algorithm h...
As a learning algorithm of feed-forward neural networks, the error reverse propagation learning (BP,...
A adaptive back-propagation algorithm for multilayered feedforward perceptrons was discussed. It was...
Generalized delta rule, popularly known as back-propagation (BP) [9, 5] is probably one of the most ...
Pattern recognition is one of the most difficult problems and somehow impossible to be solved by con...
The back-propagation (BP) training scheme is widely used for training network models in cognitive sc...
The back-propagation learning algorithm for multi-layered neural networks, which is often successful...
Abstract — Stochastic resonance is observed when noise added to a system improves the systems perfor...
In this study, an error black propagation scheme with periodic chaos neuron model is proposed using ...
For neural networks, back-propagation is a traditional, efficient and popular learning algorithm tha...
The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of imp...
Back-Propagation (BP)[Rumelhart et al, 1986] is a popular algorithm employed for training multilayer...
The error backpropagation learning algorithm (BP) is generally considered biologically implausible b...
Back Propagation (BP) was introduced by Rumelhart in 1986 [1]. BP is used for learning algorithm of ...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
Abstract — In the area of artificial neural networks, the Back Propagation (BP) learning algorithm h...
As a learning algorithm of feed-forward neural networks, the error reverse propagation learning (BP,...
A adaptive back-propagation algorithm for multilayered feedforward perceptrons was discussed. It was...
Generalized delta rule, popularly known as back-propagation (BP) [9, 5] is probably one of the most ...
Pattern recognition is one of the most difficult problems and somehow impossible to be solved by con...
The back-propagation (BP) training scheme is widely used for training network models in cognitive sc...
The back-propagation learning algorithm for multi-layered neural networks, which is often successful...
Abstract — Stochastic resonance is observed when noise added to a system improves the systems perfor...
In this study, an error black propagation scheme with periodic chaos neuron model is proposed using ...
For neural networks, back-propagation is a traditional, efficient and popular learning algorithm tha...
The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of imp...
Back-Propagation (BP)[Rumelhart et al, 1986] is a popular algorithm employed for training multilayer...
The error backpropagation learning algorithm (BP) is generally considered biologically implausible b...