In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.

Voice over Internet Protocol (VoIP) is important technology that’s rapidly growing in the wireless networks. The Quality of Service (QoS) and Capacity are two of the most important issues that still need to be researched on wireless VoIP. The main aim of this paper is to analysis the performance of the VoIP application in wireless networks, with respect to different transport layer protocols and audio codec. Two scenarios used in the simulation stage. In the first scenario VoIP with codec G.711 transmitted over User Datagram Protocol (UDP), Stream Control Transmission Protocol (SCTP), and Real-Time Transport Protocol (RTP). While, in the second scenario VoIP with codec G.726 transmitted over UDP, SCTP, and RTP protocols. Network simulator

Planning of electrical distribution networks is considered of highest priority at the present time in Iraq, due to the huge increase in electrical demand and expansions imposed on distribution networks as a result of the great and rapid urban development.

Distribution system planning simulates and studies the behavior of electrical distribution networks under different operating conditions. The study provide understanding of the existing system and to prepare a short term development plan or a long term plan used to guide system expansion and future investments needed for improved network performance.

The objective of this research is the planning of Al_Bayaa 11 kV distribution network in Baghdad city bas

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

In recent years, the Global Navigation Satellite Services (GNSS) technology has been frequently employed for monitoring the Earth crust deformation and movement. Such applications necessitate high positional accuracy that can be achieved through processing GPS/GNSS data with scientific software such as BERENSE, GAMIT, and GIPSY-OSIS. Nevertheless, these scientific softwares are sophisticated and have not been published as free open source software. Therefore, this study has been conducted to evaluate an alternative solution, GNSS online processing services, which may obtain this privilege freely. In this study, eight years of GNSS raw data for TEHN station, which located in Iran, have been downloaded from UNAVCO website

The present search aims to develop a test for selective attention, cognitive load and thinking mistakes and measuring these concepts among Baghdad university students. To make a comparison between the selective attention, cognitive load, and the mistakes of thinking among students in term of gender. To identify the relationship among the selective attention, cognitive load and the mistakes of thinking of university students. To achieve these purposes, the searcher has developed a test for selective attention, cognitive load, and the mistakes of thinking. Then, these tools were applied to a sample of (200) university students were selected from (21) college. The researcher used t-test of one sample, t-test of two independent

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p