This paper addresses the problem of neural computing by a fundamentally different approach to the one currently adopted in digital computers. The approach is based on the experience, rather than on the specification of operators as it is done in the conventional mathematical approach and it is well suited for implementation by neural networks
This book presents and studies a class of stochastic models for biological neural nets. A biological...
We investigate a novel neural network model which uses stochastic weights. It is shown that the func...
In recent years, hardware implementation of neural networks has received increasing attention from r...
This paper addresses the problem of neural computing by a fundamentally different approach to the on...
Abstract. Artificial neural networks are brain-like models of parallel computations and cognitive ph...
Artificial Neural Networks (ANNs) must be able to learn by experience from environment. This propert...
Artificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biol...
Artificial neural networks are brain-like models of parallel computations and cognitive phenomena. W...
[eng] This paper presents a new methodology for the hardware implementation of neural networks (NNs)...
Our interest is in computers called articial neural networks. These consist of assemblies of simple ...
This book describes a large number of open problems in the theory of stochastic neural systems, with...
This article describes a novel neural stochastic model for solving graph problems. The neural system...
This paper analyzes the criteria for the direct correspondence between a deterministic neural networ...
Most Artificial Neural Networks that are widely used today focus on approximating deterministic inpu...
Artificial neural networks (ANNs) are widely used as "black-box" models of complex processes and sys...
This book presents and studies a class of stochastic models for biological neural nets. A biological...
We investigate a novel neural network model which uses stochastic weights. It is shown that the func...
In recent years, hardware implementation of neural networks has received increasing attention from r...
This paper addresses the problem of neural computing by a fundamentally different approach to the on...
Abstract. Artificial neural networks are brain-like models of parallel computations and cognitive ph...
Artificial Neural Networks (ANNs) must be able to learn by experience from environment. This propert...
Artificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biol...
Artificial neural networks are brain-like models of parallel computations and cognitive phenomena. W...
[eng] This paper presents a new methodology for the hardware implementation of neural networks (NNs)...
Our interest is in computers called articial neural networks. These consist of assemblies of simple ...
This book describes a large number of open problems in the theory of stochastic neural systems, with...
This article describes a novel neural stochastic model for solving graph problems. The neural system...
This paper analyzes the criteria for the direct correspondence between a deterministic neural networ...
Most Artificial Neural Networks that are widely used today focus on approximating deterministic inpu...
Artificial neural networks (ANNs) are widely used as "black-box" models of complex processes and sys...
This book presents and studies a class of stochastic models for biological neural nets. A biological...
We investigate a novel neural network model which uses stochastic weights. It is shown that the func...
In recent years, hardware implementation of neural networks has received increasing attention from r...
DOI
Add to ORKG