Add to ORKGThe paper deals with the application of CUDA-technology on software implementation of direct and reverse passes of artificial neural network (ANN) based on back-propagation algorithm. It is shown that introduction of «imaginary» neurons helps to adapt the topology of the neural network to be trained and calculated with CUDA-technology. It is proved that «imaginary» neurons do not affect on the calculations in the back propagation algorithm
English In this thesis we are concerned with the hardware implementation of learning algorithms for...
This project presented a backpropagation neural network on FPGA which can conduct inference and tra...
The problem of computing machine passing the maze is one of theoretical computer science key tasks. ...
Artificial Neural Networks (ANNs) are computer software programs that mimic the human brain's abilit...
A parallel Back-Propagation(BP) neural network training technique using Compute Unified Device Archi...
The object of research is to parallelize the learning process of artificial neural networks to autom...
The object of research is to parallelize the learning process of artificial neural networks to autom...
Inspired by biological neural networks, Artificial neural networks are massively parallel computing ...
Artificial neural networks may probably be the single most successful technology in the last two dec...
The first successful implementation of Artificial Neural Networks (ANNs) was published a little over...
This paper presents some experimental results on the realization of a parallel simulation of an Arti...
Abstract. This work presents the implementation of Feedforward Multi-Layer Perceptron (FFMLP) Neural...
Artificial neural net (ANN) models, or neural networks, connectionist models, parallel distributed p...
Algorithms, applications and hardware implementations of neural networks are not investigated in clo...
This thesis presents a digital hardware implementation of an artificial neuron with learning ability...
English In this thesis we are concerned with the hardware implementation of learning algorithms for...
This project presented a backpropagation neural network on FPGA which can conduct inference and tra...
The problem of computing machine passing the maze is one of theoretical computer science key tasks. ...
Artificial Neural Networks (ANNs) are computer software programs that mimic the human brain's abilit...
A parallel Back-Propagation(BP) neural network training technique using Compute Unified Device Archi...
The object of research is to parallelize the learning process of artificial neural networks to autom...
The object of research is to parallelize the learning process of artificial neural networks to autom...
Inspired by biological neural networks, Artificial neural networks are massively parallel computing ...
Artificial neural networks may probably be the single most successful technology in the last two dec...
The first successful implementation of Artificial Neural Networks (ANNs) was published a little over...
This paper presents some experimental results on the realization of a parallel simulation of an Arti...
Abstract. This work presents the implementation of Feedforward Multi-Layer Perceptron (FFMLP) Neural...
Artificial neural net (ANN) models, or neural networks, connectionist models, parallel distributed p...
Algorithms, applications and hardware implementations of neural networks are not investigated in clo...
This thesis presents a digital hardware implementation of an artificial neuron with learning ability...
English In this thesis we are concerned with the hardware implementation of learning algorithms for...
This project presented a backpropagation neural network on FPGA which can conduct inference and tra...
The problem of computing machine passing the maze is one of theoretical computer science key tasks. ...