Add to ORKGABSTRACT A new fast training algorithm for the Multilayer Perceptron (MLP) is proposed. This new algorithm is based on the optimization of a mixed Least Square (LS) and a Least Fourth (LF) criterion producing a modified form of the standard back propagation algorithm (SBP). To determine the updating rules in the hidden layers, an analogous back propagation strategy used in the conventional learning algorithms is developed. This permits the application of the learning procedure to all the layers. Experimental results on benchmark applications and a real medical problem are obtained which indicates significant reduction in the total number of iterations, the convergence time, and the generalization capacity when compared to those of the SBP a...
Several neural network architectures have been developed over the past several years. One of the mos...
We propose a fast learning method for multilayer perceptrons (MLPs) on large vocabulary continuous s...
Abstract:- We present in this article a new approach for multilayer perceptrons ’ training. It is ba...
A new training algorithm is presented as a fast alternative to the backpropagation method. The new a...
Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain. This p...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
In this paper a review of fast-learning algorithms for multilayer neural networks is presented. From...
The multilayer perceptron is one of the most commonly used types of feedforward neural networks and ...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
In this paper, the authors propose a new training algorithm which does not only rely upon the traini...
A adaptive back-propagation algorithm for multilayered feedforward perceptrons was discussed. It was...
Neural networks as a general mechanism for learning and adaptation became increasingly popular in re...
This paper presents two compensation methods for multilayer perceptrons (MLPs) which are very diffic...
this report also have been published on ESANN '93 [Schiffmann et al., 1993]. The dataset used i...
Several neural network architectures have been developed over the past several years. One of the mos...
We propose a fast learning method for multilayer perceptrons (MLPs) on large vocabulary continuous s...
Abstract:- We present in this article a new approach for multilayer perceptrons ’ training. It is ba...
A new training algorithm is presented as a fast alternative to the backpropagation method. The new a...
Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain. This p...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
In this paper a review of fast-learning algorithms for multilayer neural networks is presented. From...
The multilayer perceptron is one of the most commonly used types of feedforward neural networks and ...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
In this paper, the authors propose a new training algorithm which does not only rely upon the traini...
A adaptive back-propagation algorithm for multilayered feedforward perceptrons was discussed. It was...
Neural networks as a general mechanism for learning and adaptation became increasingly popular in re...
This paper presents two compensation methods for multilayer perceptrons (MLPs) which are very diffic...
this report also have been published on ESANN '93 [Schiffmann et al., 1993]. The dataset used i...
Several neural network architectures have been developed over the past several years. One of the mos...
We propose a fast learning method for multilayer perceptrons (MLPs) on large vocabulary continuous s...
Abstract:- We present in this article a new approach for multilayer perceptrons ’ training. It is ba...