Multi layer perceptron with back propagation algorithm is popular and more used than other neural network types in various fields of investigation as a non-linear predictor. Though MLP can solve complex and non-linear problems, it cannot use missing data for training directly. We propose a training algorithm with incomplete pattern data using conventional MLP network. Focusing on the fact that ...

We have proposed the glial network which was inspired from the feature of brain. In the glial network, glias generate independent oscillations and these oscillations propagated neurons and other glias. We confirmed that the glial network improved the learning performance of the Multi-Layer Perceptron (MLP) In this article, we investigate the MLP with the impulse glial network. The glias have on...

Abstract—A glia is a nervous cell in the brain. Currently, the glia is known as a important cell for the human’s cerebration. Because the glia transmits signals to neurons and other glias. We notice features of the glia and consider to apply it for an artificial neural network. In this paper, we propose a Multi-layer perceptron (MLP) with pulse glial chain. The pulse glial chain is inspired fro...

In this paper, we introduce a method that allows to evaluate efficiently the “importance” of each coordinate of the input vector of a neural network. This measurement can be used to obtain informations about the studied data. It can also be used to suppress irrelevant inputs in order to speed up the classification process conducted by the network.

in this paper, the grouting ability of sandy soils is investigated by artificial neural networks based on the results of chemical grout injection tests. in order to evaluate the soil grouting potential, experimental samples were prepared and then injected. the sand samples with three different particle sizes (medium, fine, and silty) and three relative densities (%30, %50, and %90) were injecte...

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...

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