In this contribution is investigated the use of multilayer perceptron type neural networks in the characterization of images by texture content. The paper is focused on the effects of textural feature extraction methods on the network architecture, training performance and generalization capability when applied in indexing of images contained within multimedia image databases. An in depth exper...

This paper presents a method of recognition of isolated offline handwritten Devanagari numerals using wavelets and neural network classifier. This method of optical character recognition takes the handwritten numeral image as input. After pre-processing, it is subjected to single level wavelet decomposition using Daubechies-4 wavelet filter. This wavelet decomposition allows viewing the input n...

Variational encoder-decoders (VEDs) have shown promising results in dialogue generation. However, the latent variable distributions are usually approximated by a much simpler model than the powerful RNN structure used for encoding and decoding, yielding the KL-vanishing problem and inconsistent training objective. In this paper, we separate the training step into two phases: The first phase lea...

In this contribution we describe the modelling of the differential pressure behavior of isolation materials at a sieve by artificial neural networks (ANN). The subject arranges itself in the area of the reactor safety research. Compared with [3] the number of the inputs for the connection which can be modelled was increased. Thereby the number of necessary connections is reduced and the model q...

This paper gives the definition of Transparent Neural Network “TNN” for the simulation of the globallocal vision and its application to the segmentation of administrative document image. We have developed and have adapted a recognition method which models the contextual effects reported from studies in experimental psychology. Then, we evaluated and tested the TNN and the multi-layer perceptron...

In this paper we propose and analyze a multilayer perceptron-like model with matrix inputs. We applied the proposed model to the financial time series prediction problem, compared it with the standard multilayer perceptron model, and got fairly good results.

The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausi...

The multilayer perceptron neural network has proved to be a very effective tool for the classification of remote-sensing images. Unfortunately, the training of such a classifier by using data with very different a priori class probabilities Ž . imbalanced data is very slow. This paper describes a learning technique aimed at speeding up the training of a multilayer perceptron when applied to imb...

Radial Basis function Neural Networks forms a class of neural networks which is much more advantageous then other methods of neural networks such as faster learning, easy networks & structures & better approximations & classifications. The system consist of a multilayer perceptron (MLP)-like network that performs image segmentation by RBF technique of the input image using labels automatically ...

The system consist of a multilayer perceptron (MLP)-like network that performs image segmentation by RBF technique of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, the learning is unsupervised. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information con...