This study presents a novel training algorithm depending upon the recently proposed Fitness Dependent Optimizer (FDO). The stability of this has been verified and performance-proofed in both exploration exploitation stages using some standard measurements. influenced our target to gauge performance multilayer perceptron neural networks (MLP). combines FDO with MLP (codename FDO-MLP) for optimiz...

Nowadays, news media seems to have been digitized. One of them is printed which has now turned into online news. The increasing use social made people interested in reading online. News needs attract readers with their headlines. Various businesses want know the future demand readers, as well whether released can reach more so that becomes popular. Therefore, interest today, this paper will ana...

A genetic algorit hm is proposed for the training and construction of a multilayer perceptron. The genetic algorit hm works on a layer-by-layer basis. For each layer, it automat ically chooses the number of neurons required, computes the synaptic weights between the present layer of neurons and the next layer, and gives a set of training patterns for the succeeding layer. The algorithm presente...

A vascular necrosis (AVN) of the femoral head is a common yet potentially serious disorder which can be detected in its very early stages with magnetic resonance imaging. We have developed multi-layer perceptron networks, trained with conjugate gradient optimization, which diagnose A VN from single magnetic resonance images of the femoral head with 100% accuracy on training data and 97% accurac...

-This paper presents a mathematical analysis of the occurrence of temporary minima during training of a single-output, two-layer neural network, with learning according to the back-propagation algorithm. A new vector decomposition method is introduced, which simplifies the mathematical analysis of learning of neural networks considerably. The analysis shows that temporary minima are inherent to...

The problem of classifying objects from their ultrasonic signature for robotic applications is studied in this paper. The system developed utilises the spatial diversity of a four element linear array transducer to enhance classification performance. A signal pre-processing technique employing time domain envelope detection in combination with a multi-layer perceptron neural network has yielded...

The paper proposes a perceptron based artificial neural network model for diagnosing learning disability using curriculum based test conducted by special educators in medical environment. The model comprises of a single input layer with eleven units which correspond to different sections of a conventional test and one output unit. The method is not only devoid of typical computational complexit...

Ole Winther CONNECT The Niels Bohr Institute Blegdamsvej 17 2100 Copenhagen, Denmark wintherGconnect.nbi.dk We present an algorithm which is expected to realise Bayes optimal predictions in large feed-forward networks. It is based on mean field methods developed within statistical mechanics of disordered systems. We give a derivation for the single layer perceptron and show that the algorithm a...