Graphics processor unit hardware acceleration of Levenberg-Marquardt artificial neural network training

This paper makes two principal contributions. The first is that there appears to be no previous a description in the research literature of an artificial neural network implementation on a graphics processor unit (GPU) that uses the Levenberg-Marquardt (LM) training method. The second is an initial attempt at determining when it is computationally beneficial to exploit a GPU’s parallel nature in preference to the traditional implementation on a central processing unit (CPU). The paper describes the approach taken to successfully implement the LM method, discusses the advantages of this approach for GPU implementation and presents results that compare GPU and CPU performance on two test data sets

ARTIFICIAL NEURAL NETWORKS PRUNING APPROACH FOR GEODETIC VELOCITY FIELD DETERMINATION

There has been a need for geodetic network densification since the early days oftraditional surveying. In order to densify geodetic networks in a way that willproduce the most effective reference frame improvements, the crustal velocity fieldmust be modelled. Artificial Neural Networks (ANNs) are widely used as functionapproximators in diverse fields of geoinformatics including velocity fielddetermination. Deciding the number of hidden neurons required for theimplementation of an arbitrary function is one of the major problems of ANN thatstill deserves further exploration. Generally, the number of hidden neurons isdecided on the basis of experience. This paper attempts to quantify the significanceof pruning away hidden neurons in ANN architecture for velocity fielddetermination. An initial back propagation artificial neural network (BPANN) with30 hidden neurons is educated by training data and resultant BPANN is applied ontest and validation data. The number of hidden neurons is subsequently decreased,in pairs from 30 to 2, to achieve the best predicting model. These pruned BPANNsare retrained and applied on the test and validation data. Some existing methods forselecting the number of hidden neurons are also used. The results are evaluated interms of the root mean square error (RMSE) over a study area for optimizing thenumber of hidden neurons in estimating densification point velocity by BPANN

Elagage d'un perceptron multicouches : utilisation de l'analyse de la variance de la sensibilit\'e des param\`etres

The stucture determination of a neural network for the modelisation of a system remain the core of the problem. Within this framework, we propose a pruning algorithm of the network based on the use of the analysis of the sensitivity of the variance of all the parameters of the network. This algorithm will be tested on two examples of simulation and its performances will be compared with three other algorithms of pruning of the literatureComment: 6 page

Computational models and approaches for lung cancer diagnosis

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results