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...

The purpose of this study is to develop subject categorization methods for educational resources using multilayer perceptron (MLP) and to examine the performance of the test documents as an application system. To examine the performance two methods are examined: Latent Semantic Indexing method (LSI) and a three layer feedforward network as a simple MLP. The document vectors were estimated by th...

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...

`Only for the MLP do we include the hourly weather data as part of the input space, resulting in 25 features. And for the ResNet model, we one-hot encode day of the week and month into our input space, resulting in 41 initial input features. MULTILAYER PERCEPTRON (MLP) Our baseline of comparison is a basic MLP that consists of three fully connected layers, containing a hidden layer with 24 neur...

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...

Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, AR...

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. ...

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 ...

We define a Gamma multi-layer perceptron (MLP) as an MLP with the usual synaptic weights replaced by gamma filters (as proposed by de Vries and Principe (de Vries and 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 find that both the inclusion of gamma filters in all layer...

This article describes a new approach to the automated construction of a distributed neural classifier. The methodology is based upon supervised hierarchical clustering which enables one to determine reliable regions in the representation space. The proposed methodology proceeds by associating each of these regions with a Multi-Layer Perceptron (MLP). Each MLP has to recognise elements inside i...