Add to ORKGA positive reinforcement type learning algorithm is formulated for a stochastic feed-forward multilayer neural network, with far interlayer synaptic connections, and we obtain a learning rule similar to that of the Boltzmann machine on the same multilayer structure. By applying a mean field approximation to the stochastic feed-forward neural network, the generalized error back-propagation learning rule is derived for a deterministic analog feed-forward multilayer network with the far interlayer synaptic connections
In this paper a review of fast-learning algorithms for multilayer neural networks is presented. From...
A new methodology for neural learning is presented, whereby only a single iteration is required to t...
The revival of multilayer neural networks in the mid 80's originated from the discovery of the ...
A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward neural ...
We have found a more general formulation of the REINFORCE learning principle which had been proposed...
Introduction The work reported here began with the desire to find a network architecture that shared...
Abstract—We have found a more general formulation of the REINFORCE learning principle which had been...
We introduce a biologically plausible method of implementing reinforcementlearning to multi-layer ne...
Theoretical study about neural networks, especially their types of topologies and networks learning....
A adaptive back-propagation algorithm for multilayered feedforward perceptrons was discussed. It was...
The training of multilayer perceptron is generally a difficult task. Excessive training times and la...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
Reinforcement learning algorithms comprise a class of learning algorithms for neural networks. Reinf...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
In this paper a review of fast-learning algorithms for multilayer neural networks is presented. From...
A new methodology for neural learning is presented, whereby only a single iteration is required to t...
The revival of multilayer neural networks in the mid 80's originated from the discovery of the ...
A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward neural ...
We have found a more general formulation of the REINFORCE learning principle which had been proposed...
Introduction The work reported here began with the desire to find a network architecture that shared...
Abstract—We have found a more general formulation of the REINFORCE learning principle which had been...
We introduce a biologically plausible method of implementing reinforcementlearning to multi-layer ne...
Theoretical study about neural networks, especially their types of topologies and networks learning....
A adaptive back-propagation algorithm for multilayered feedforward perceptrons was discussed. It was...
The training of multilayer perceptron is generally a difficult task. Excessive training times and la...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
Reinforcement learning algorithms comprise a class of learning algorithms for neural networks. Reinf...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
In this paper a review of fast-learning algorithms for multilayer neural networks is presented. From...
A new methodology for neural learning is presented, whereby only a single iteration is required to t...
The revival of multilayer neural networks in the mid 80's originated from the discovery of the ...