Add to ORKGThis paper analyses the parallel implementation using networks of transputers of a neural structure belonging to a particular class of neural architectures known as GSN neural networks. These architectures, belonging to the general clasa of RAM-based networks and composed 01 digitally specified processing nodes, have been implemented using different processing topologies, and performance in relatíon to both training and testing efficiency in a practical pattern recognition task has been evaluated.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
This paper presents a parallel architecture for a radial basis function (RBF) neural network used fo...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
This paper analyses the parallel implementation using networks of transputers of a neural structure ...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
This thesis presents a detailed study of the parallel implementations of backpropagation neural netw...
This paper presents some experimental results on the realization of a parallel simulation of an Arti...
This thesis is about parallelizing the training phase of a feed-forward, artificial neural network....
Long training times and non-ideal performance have been a big impediment in further continuing the u...
It seems to be an everlasting discussion. Spending a lot of additional time and extra money to imple...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
Artificial neural networks have applications in many fields ranging from medicine to image processin...
The work presented in this thesis is mainly involved in the study of Artificial Neural Networks (ANN...
This paper presents a parallel architecture for a radial basis function (RBF) neural network used fo...
Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
This paper presents a parallel architecture for a radial basis function (RBF) neural network used fo...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
This paper analyses the parallel implementation using networks of transputers of a neural structure ...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
This thesis presents a detailed study of the parallel implementations of backpropagation neural netw...
This paper presents some experimental results on the realization of a parallel simulation of an Arti...
This thesis is about parallelizing the training phase of a feed-forward, artificial neural network....
Long training times and non-ideal performance have been a big impediment in further continuing the u...
It seems to be an everlasting discussion. Spending a lot of additional time and extra money to imple...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
Artificial neural networks have applications in many fields ranging from medicine to image processin...
The work presented in this thesis is mainly involved in the study of Artificial Neural Networks (ANN...
This paper presents a parallel architecture for a radial basis function (RBF) neural network used fo...
Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face...
Parallelizing neural networks is an active area of research. Current approaches surround the paralle...
This paper presents a parallel architecture for a radial basis function (RBF) neural network used fo...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...