We consider the construction of minimal multilayered perceptrons for solving combinatorial optimization problems. Though general in nature, the proposed construction method is presented as a case study for the sorting problem. The presentation starts with an Full-size image (<1 K) three-layered perceptron based on complete enumeration, that solves the sorting problem of n numbers. This network is then gradually reduced to an Full-size image (<1 K) three-layered perceptron, which can be viewed as a neural implementation of Preparata's parallel enumerative sorting algorithm
The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limit...
The Letter reports the benefits of decomposing the multilayer perceptron (MLP) for pattern recogniti...
: combinatorial optimization is an active field of research in Neural Networks. Since the first atte...
We consider the construction of minimal multilayered perceptrons for solving combinatorial optimizat...
We consider the construction of minimal multi-layered perceptrons for solving combinatorial optimiza...
In this paper a three-layered perceptron is derived that exactly solves the problem of sorting n num...
In this paper we consider the problem of determining the minimal number of layers required by a mult...
We investigate the network complexity of multilayered perceptrons for solving exactly a given proble...
We investigate the network complexity of multi-layered perceptrons for solving ex-actly a given prob...
[[abstract]]A multilayer feedforward neural network is proposed to solve sorting problems. The netwo...
This paper presents a new constructive algorithm to design multilayer perceptron networks used as cl...
Sorting networks are an interesting class of parallel sorting algorithms with applications in multi-...
Ellerbrock TM. Multilayer neural networks : learnability, network generation, and network simplifica...
[[abstract]]Neural network is a proper model for parallel computing because it can process the data ...
Abstract. Sorting Networks (SN) are efficient tools to sort an input data sequence. They are compose...
The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limit...
The Letter reports the benefits of decomposing the multilayer perceptron (MLP) for pattern recogniti...
: combinatorial optimization is an active field of research in Neural Networks. Since the first atte...
We consider the construction of minimal multilayered perceptrons for solving combinatorial optimizat...
We consider the construction of minimal multi-layered perceptrons for solving combinatorial optimiza...
In this paper a three-layered perceptron is derived that exactly solves the problem of sorting n num...
In this paper we consider the problem of determining the minimal number of layers required by a mult...
We investigate the network complexity of multilayered perceptrons for solving exactly a given proble...
We investigate the network complexity of multi-layered perceptrons for solving ex-actly a given prob...
[[abstract]]A multilayer feedforward neural network is proposed to solve sorting problems. The netwo...
This paper presents a new constructive algorithm to design multilayer perceptron networks used as cl...
Sorting networks are an interesting class of parallel sorting algorithms with applications in multi-...
Ellerbrock TM. Multilayer neural networks : learnability, network generation, and network simplifica...
[[abstract]]Neural network is a proper model for parallel computing because it can process the data ...
Abstract. Sorting Networks (SN) are efficient tools to sort an input data sequence. They are compose...
The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limit...
The Letter reports the benefits of decomposing the multilayer perceptron (MLP) for pattern recogniti...
: combinatorial optimization is an active field of research in Neural Networks. Since the first atte...