The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

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The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

The aim of this study was to evaluate tensile properties of low and medium carbon ferrite -martensite dual phase steel, and the effect cryogenic treatment at liquid nitrogen temperature (-196 ºC) on its properties. Low carbon steel (C12D) and medium carbon steels (C32D & C42D) were used in this work. For each steel grade, five groups of specimens were prepared according to the type of heat treatment. The first group was normalized, the second group was normalized and subsequently subjected to cryogenic treatment then tempered at (200 ºC) for one hour, the third group was quenched from intercritical annealing temperature of (760 ºC) to obtain dual phase (DP) steel, the fourth and fifth groups were both quenched from (760 ºC), but

There are no single materials which can withstand all the extreme operating conditions in modern technology.  Protection of the metals from hostile environments has therefore become a technical and economic necessity.  

In this work, for enhancing their wear-resistance, boride layers were deposited on the surface of low carbon steel by a pack cementation method at 850 °C for (2, 4, and 6) h using vacuum furnace. The boronizing process was achieved using different concentration of boron source (20, 25, and 30) % wt. into coating mixture to optimize the best conditions which ensure the higher properties with lower time. The coating was characteristic by X ray diffraction (XRD), and it is confirmed t

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p