In this paper ordered neural networks for the Nbit parity function containing [log2(N + 1)] threshold elements are constructed. The minimality of this network is proved. The connection between minimum perceptrons of Gamb for the N-bit parity function and one combinatorial problems is established
This paper deals with a neural network model in which each neuron performs a threshold logic functio...
We show that neural networks with three-times continuously differentiable activation functions are c...
In this paper, the ability of a Binary Neural Network comprising only neurons with zero thresholds a...
www.elsevier.com/locate/neucom N-bit parity neural networks:new solutions based on linear programmin
Abstract:- Highly nonlinear data sets are important in the field of artificial neural networks. It i...
Starting with two hidden units, we train a simple single hidden layer feed-forward neural network to...
Abstract. A universal binary neuron (UBN) operates with the complex-valued weights and the complex-v...
The paper overviews results dealing with the approximation capabilities of neural networks, and boun...
Experimental evidence has shown analog neural networks to be ex-~mely fault-tolerant; in particular....
An algorithm for the training of a special multilayered feed-forward neural network is presented. Th...
The paper will show that in order to obtain minimum size neural networks (i.e., size-optimal) for im...
A universal binary neuron (UBN) operates with complex-valued weights and a complex-valued activation...
Linear threshold elements are the basic building blocks of artificial neural networks. A linear thre...
We consider the construction of minimal multi-layered perceptrons for solving combinatorial optimiza...
We consider the construction of minimal multilayered perceptrons for solving combinatorial optimizat...
This paper deals with a neural network model in which each neuron performs a threshold logic functio...
We show that neural networks with three-times continuously differentiable activation functions are c...
In this paper, the ability of a Binary Neural Network comprising only neurons with zero thresholds a...
www.elsevier.com/locate/neucom N-bit parity neural networks:new solutions based on linear programmin
Abstract:- Highly nonlinear data sets are important in the field of artificial neural networks. It i...
Starting with two hidden units, we train a simple single hidden layer feed-forward neural network to...
Abstract. A universal binary neuron (UBN) operates with the complex-valued weights and the complex-v...
The paper overviews results dealing with the approximation capabilities of neural networks, and boun...
Experimental evidence has shown analog neural networks to be ex-~mely fault-tolerant; in particular....
An algorithm for the training of a special multilayered feed-forward neural network is presented. Th...
The paper will show that in order to obtain minimum size neural networks (i.e., size-optimal) for im...
A universal binary neuron (UBN) operates with complex-valued weights and a complex-valued activation...
Linear threshold elements are the basic building blocks of artificial neural networks. A linear thre...
We consider the construction of minimal multi-layered perceptrons for solving combinatorial optimiza...
We consider the construction of minimal multilayered perceptrons for solving combinatorial optimizat...
This paper deals with a neural network model in which each neuron performs a threshold logic functio...
We show that neural networks with three-times continuously differentiable activation functions are c...
In this paper, the ability of a Binary Neural Network comprising only neurons with zero thresholds a...
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