Add to ORKGA positive reinforcement type learning algorithm is formulated for a stochastic feed-forward neural network, and a learning equation similar to that of the Boltzmann machine algorithm is obtained. By applying a mean field approximation to the same stochastic feed-forward neural network, a deterministic analog feed-forward network is obtained and the back-propagation learning rule is re-derived
Theoretical study about neural networks, especially their types of topologies and networks learning....
The problem of adjusting the weights (learning) in multilayer feedforward neural networks (NN) is kn...
As a learning algorithm of feed-forward neural networks, the error reverse propagation learning (BP,...
A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward multila...
Introduction The work reported here began with the desire to find a network architecture that shared...
We have found a more general formulation of the REINFORCE learning principle which had been proposed...
Abstract—We have found a more general formulation of the REINFORCE learning principle which had been...
We show how a feed-forward neural network can be sucessfully trained by using a simulated annealing ...
Abstract. Artificial neural networks are brain-like models of parallel computations and cognitive ph...
Rumelhart, Hinton and Williams [Rumelhart et al. 86] describe a learning procedure for layered netwo...
Artificial (or biological) Neural Networks must be able to form by learning internal memory of the e...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
Neural Network models have received increased attention in the recent years. Aimed at achieving huma...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
The revival of multilayer neural networks in the mid 80's originated from the discovery of the ...
Theoretical study about neural networks, especially their types of topologies and networks learning....
The problem of adjusting the weights (learning) in multilayer feedforward neural networks (NN) is kn...
As a learning algorithm of feed-forward neural networks, the error reverse propagation learning (BP,...
A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward multila...
Introduction The work reported here began with the desire to find a network architecture that shared...
We have found a more general formulation of the REINFORCE learning principle which had been proposed...
Abstract—We have found a more general formulation of the REINFORCE learning principle which had been...
We show how a feed-forward neural network can be sucessfully trained by using a simulated annealing ...
Abstract. Artificial neural networks are brain-like models of parallel computations and cognitive ph...
Rumelhart, Hinton and Williams [Rumelhart et al. 86] describe a learning procedure for layered netwo...
Artificial (or biological) Neural Networks must be able to form by learning internal memory of the e...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
Neural Network models have received increased attention in the recent years. Aimed at achieving huma...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
The revival of multilayer neural networks in the mid 80's originated from the discovery of the ...
Theoretical study about neural networks, especially their types of topologies and networks learning....
The problem of adjusting the weights (learning) in multilayer feedforward neural networks (NN) is kn...
As a learning algorithm of feed-forward neural networks, the error reverse propagation learning (BP,...