This article presents the role of artificial neural networks in use of hydroabrassive suspensive jet cut process in laminate treatment. Three-ply layer perceptron type network with an error backpropagation learning algorithm was applied to describe this process. The article provides detailed description of neural network. This neural network simulates the treatment process and predicts its effi...

Objective(s): This study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ANN) and correlation using experimental data. Methods: Two-layer perceptron feedforward artificial neural network and backpropagation Levenberg-Marquardt (BP-LM) tra...

Artificial neural network models have been studied for many years with the hope of designing information proeessing systems to produee human-like performance. The present artiele provides an introduction to neural computing by reviewing three commonly used models (namely, Hopfield's model, Kobonen's model and the Multi-layer perceptron) and showing their applications in various fields such as p...

Convolutional neural networks (CNNs) are flexible, biologically-inspired variants of multi-layer perceptrons that have proven themselves to be exceptionally suited to discriminative vision tasks. However, relatively little is known on whether they can be made both more efficient and more accurate, by introducing suitable transformations that exploit general knowledge of the target classes. We d...

The objective of this study is to investigate the correlation between the internal topological organization in neural network and the learning ability of the neural network. This study is motivated by the interesting neurophysiological examination that shows the significance of topographic map of adult mammals’brains to their learning ability and plasticity. In this study we propose a model of ...

abstract recent interest in describing the spatial distribution patterns of weeds through using interpolation methods has increased to estimate weed seedling density from spatially refferenced data and evaluation of applicable to site-specific weed management. in this research, a multi layer perceptron neural network (mlpnn) model was developed to predict the spatial distribution of h. glaucum ...

This paper introduces the notion of the variadic neural network (VNN). The inputs to a variadic network are an arbitrary-length list of n-tuples of real numbers, where n is fixed. In contrast to a recurrent network which processes a list sequentially, typically being affected more by more recent list elements, a variadic network processes the list simultaneously and is affected equally by all l...

The paper presents the application of the hybrid neural network to the solution of the calibration problem of the solid state sensor array used for the gas analysis. The applied neural network is composed of two parts: the selforganizing Kohonen layer and multilayer perceptron (MLP). The role of the Kohonen layer is to perform the feature extraction of the data and MLP network fulfills role of ...