In this paper we propose a nonlinear scalar predictor based on a combination of Multi Layer Perceptron, Radial Basis Functions and Elman networks. This system is applied to speech coding in an ADPCM backward scheme. The combination of this predictors improves the results of one predictor alone. A comparative study of this three neural networks for speech prediction is also presented.

This paper is concerned with the problem of discriminating the patients suffering from schizophrenia and bipolar disorder versus control based on EEG rhythms. The EEG rhythms are used to extract a feature vector for each patient. In this paper, the large set of features included in the EEG rhythms is reduced into smaller set of features using FFT segmentation. Two classes of classifiers which a...

Estimate of sediment load is required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. In this study Artificial Neural Networks (ANNs) are employed to estimate daily suspended sediment load. Two different ANN algorithms, Multi Layer Perce...

This paper presents the performance comparison of two architectures of neural networks: multi-layer perceptron (MLP) neural networks and radial basis function (RBF) neural networks on face recognition system (FRS). We are training MLP using different variants of back-propagation (BP) algorithm. AT&T database has been used for performance comparison. The BP is gradient descent based iterative al...

We consider the problem of approximating functions from scattered data using linear superpositions of non-linearly parameterized functions. We show how the total error (generalization error) can be decomposed into two parts: an approximation part that is due to the finite number of parameters of the approximation scheme used; and an estimation part that is due to the finite number of data avail...

Our goal was to create a facial expression recognition neural network. This network would take pictures of human faces as input and to identify a specific person, a facial expression, and detect whether or not the subject's eyes were open or closed. For the purpose we used the multi-layer perceptron neural network (MLP) with backpropagation (BP) as a reference platform to determine this archite...

purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learni...

The problem of the rejection of patterns not belonging to identiied training classes is investigated with respect to multilayer perceptron networks (MLPs). The reason for the inherent unreliability of the standard MLP in this respect is explained and some mechanisms for the enhancement of its rejection performance are considered. Two network conngurations are presented as candidates for a more ...

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fis...

Partial differential equations (PDEs) with boundary conditions (Dirichlet or Neumann) defined on boundaries with simple geometry have been successfully treated using sigmoidal multilayer perceptrons in previous works. This article deals with the case of complex boundary geometry, where the boundary is determined by a number of points that belong to it and are closely located, so as to offer a r...