In this paper, we propose to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We chose to combine two transfer functions for the nodes of the hidden layer, and to use a temporal indicator (time index as input) in order to take into account the seasonal aspect of the studied time series. The results of the prediction concern two years of me...

We deene a Gamma multi-layer perceptron (MLP) as an MLP with the usual synaptic weights replaced by gamma lters (as proposed by de Vries and Principe (de Vries & Principe 1992)) and associated gain terms throughout all layers. We derive gradient descent update equations and apply the model to the recognition of speech phonemes. We nd that both the inclusion of gamma lters in all layers, and the...

The multilayer perceptron (MLP) is a well established neural network model for supervised learning problems. Furthermore, it is well known that its performance for a given problem depends crucially on appropriately selecting the MLP architecture, which is typically achieved using cross-validation. In this work, we propose an incremental Bayesian methodology to address the important problem of a...

A pruning schema is applied to Multi-Layer Perceptron (MLP) gender classi£er. MLP uses eigenvector coef£cients of the face space created by Principal Component Analysis (PCA). We show that pruning improves the initial MLP performance by preserving the most effective input while eliminating most of the units and connections. Pruning is also used as a tool to monitor which eigenvectors contribute...

In tandem systems, the outputs of multi-layer perceptron (MLP) classifiers have been successfully used as features for HMM-based automatic speech recognition. In this paper, we propose a data-driven clustered hierarchical tandem system that yields improved performance on a large-vocabulary broadcast news transcription task. The complicated global learning for a large monolithic MLP classifier i...

Artificial Neural Network (ANN) is one of the modern computational methods proposed to solve increasingly complex problems in the real world (Xie et al., 2006 and Chau, 2007). ANN is characterized by its pattern of connections between the neurons (called its architecture), its method of determining the weights on the connections (called its training, or learning, algorithm), and its activation ...

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...

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. ...