This paper develops a hybrid model combining a Hidden Markov Chain (HMC) and Multilayer Perceptrons (MLP) on the Waveletheterogeneous Index of Market Shocks (WhIMS) to identify dynamically regimes in financial turbulences. The WhIMS is an aggregate measure of volatility computed at different frequencies. We estimate the model based on a French market stock index (CAC40 Index) and compare the pr...

Recursive Feature Elimination RFE combined with feature-ranking is an effective technique for eliminating irrelevant features. In this paper, an ensemble of MLP base classifiers with feature-ranking based on the magnitude of MLP weights is proposed. This approach is compared experimentally with other popular feature-ranking methods, and with a Support Vector Classifier SVC. Experimental results...

This study uses a new data visualization method, developed by the first author, to investigate the reliability of a real world low-back-pain Multi-layer Perceptron (MLP) network from a hidden layer decision region perspective. Using decision region identification information from an explanation facility, the MLP training examples are discovered to occupy decision regions in contiguous class thr...

In order to control the trade-off between sensitivity and specificity of MLP binary classifiers, we extended the Backpropagation algorithm, in batch mode, to incorporate different misclassification costs via separation of the global mean squared error between positive and negative classes. By achieving different solutions in ROC space, our algorithm improved the MLP classifier performance on im...

The procedure of calculating Mel Frequency based Cepstral Coefficients (MFCC) is shown to resemble a three layer Multilayer Perceptron (MLP) like structure. Such an MLP is employed as a preprocessor in a hybrid HMM-MLP system, and the possibility of optimizing the whole system as a single entity, with respect to a suitable criterion, is pointed out. This system, together with the Maximum Mutual...

This paper presents an application of Multi-layer Perceptrons (MLP) neural networks to model the demographic characteristics of antenatal clinic attendees in South Africa. The method of cross-validation is used to examine the betweensample variation of neural networks for HIV prediction. MLP neural networks for classifying both the HIV negative and positive clinic attendees are developed and ev...

In this work, a recently derived recursive least-square (RLS) algorithm to train multi layer perceptron (MLP) is used in an MLP-based decision feedback equalizer (DFE) instead of the back propagation (BP) algorithm. Its performance is investigated and compared to those of MLP-DFE based on the BP algorithm and the simple DFE based on the least-mean square (LMS) algorithm. The results show improv...

In this paper, two feed forward neural network models have been presented to predict the Silicon Modification Level (SiML) of W319 aluminum alloys using the Thermal Analysis (T.A) parameters as inputs. The developed neural networks are a Multilayer Perceptron (MLP) network and a Radial Basis Function (RBF) network. The neural network models were found to predict the SiML accurately (R=0.99). Th...

Materials and methods: A data set consisting of 584 stroke patients was analyzed using MLP neural networks. Th e eff ect of prognostic factors (age, hospitalization time, sex, hypertension, atrial fi brillation, embolism, stroke type, infection, diabetes mellitus, and ischemic heart disease) on mortality in stroke were trained with 6 diff erent MLP algorithms [quick propagation (QP), Levenberg-...

Link admission control (LAC) in broadband ATM networks is based on evaluation of expected traffic performance. The traditional LAC approach relies on approximate analytical performance models, and can lead to an over controlled network. This paper presents a hybrid LAC scheme which uses a multi layer perceptron (MLP) to refine the performance estimate of a traditional analytical approximation. ...