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MODIFIED TRAINING METHOD FOR FEEDFORWARD NEURAL NETWORKS AND ITS APPLICATION in 4-LINK SCARA ROBOT IDENTIFICATION
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In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet function. This approach has been performed very successfully, with better results
obtained with the FFNN with modified wavelet activation function (FFMW) when compared with classic
FFNN with Sigmoid activation function (FFS) .One can notice from the simulation that the FFMW can be
capable of identifying the 4-Links of SCARA robot more efficiently than the classic FFS.

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Publication Date
Sat Jan 01 2011
Journal Name
Journal Of Engineering
FILTRATION MODELING USING ARTIFICIAL NEURAL NETWORK (ANN)
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In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

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Publication Date
Sat Apr 30 2022
Journal Name
Iraqi Journal Of Science
Comparison Different Estimation Method for Reliability Function of Rayleigh Distribution Based On Fuzzy Lifetime Data
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    In this study, we present different methods of estimating fuzzy reliability of a two-parameter Rayleigh distribution via the maximum likelihood estimator, median first-order statistics estimator, quartile estimator, L-moment estimator, and mixed Thompson-type estimator. The mean-square error MSE as a measurement for comparing the considered methods using simulation through different values for the parameters and unalike sample sizes is used. The results of simulation show that the fuzziness values are better than the real values for all sample sizes, as well as  the fuzzy reliability at the estimation  of the Maximum likelihood Method, and Mixed Thompson Method perform better than the other methods in the sense of MSE, so that

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Publication Date
Mon Feb 20 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Synthesis of Some New 2-thioxoimidazolidin-4-one Derivatives
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This research includes the synthesis of new series of heterocyclic compounds. Reaction of 2-nitro benzylidene)thiosemicarbazide(1) with ethyl chloro acetate gave (2-nitro benzylidene amino)-2-thioxomidazolidine-4-one(2) ,treatment(2) with methyl iodide to give(3)which was reacted with hydrazine to give 2-hydrazinyl-1-[(2-nitrobenzylidene)amino]- 1H-imidazol-5(4H)-one, andreation of compound(2) with aromatic aldehydes to give 5arylidene -3-({2-nitro benzylidene}amino)2-thioxo-3,5-dihydro-4H-imidazole-4-one(5a,5b), which was reacted with ethyl aceto acetate to give 4-aryl-1-[2-nitrobenzylidene, amino -6oxo-2-thioxo octa hydro-1H-benzo[d]imidazole-5-carboxylate and followed synthesis of βlactamederivtives(9a,9b) by treatment derivatives(

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Publication Date
Tue Dec 31 2013
Journal Name
Al-khwarizmi Engineering Journal
Design of an Adaptive PID Neural Controller for Continuous Stirred Tank Reactor based on Particle Swarm Optimization
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 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Artificial Neural Network (ANN) for Prediction of Viscosity Reduction of Heavy Crude Oil using Different Organic Solvents
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The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests  and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a  heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage  (5, 10 and  20 wt.% )  of  (n-heptane, toluene, and a mixture of  different ratio

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Publication Date
Thu Dec 01 2022
Journal Name
Baghdad Science Journal
Diagnosing COVID-19 Infection in Chest X-Ray Images Using Neural Network
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With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

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Publication Date
Sun Dec 30 2007
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Fractional Hold-Up in RDC Column Using Artificial Neural Network
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In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

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Publication Date
Mon Jun 01 2020
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Cutting Force in Turning Process by Using Artificial Neural Network
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Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

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Publication Date
Thu Jun 30 2016
Journal Name
Al-kindy College Medical Journal
Obesity and its co morbidities in children and adolescents
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Background: Obesity has become one of the most important public heath problems all over the world.An epidemic of obesity is affecting children and adolescents across the developed and developing countries in recent years. As the prevalence of obesity increased, so did the prevalence of co morbidities like metabolic and endocrine diseases.Objectives: To overview obesity clinical features and the prevalence of associated co morbidities in children and adolescents attended the obesity researches and therapy center in Alkindy medical collage.Type of study: This is a cohort observational studyMethods : Obese child and adolescents aged 4-15year attended the obesity research and therapy unit in AL Kindy medical collage from the 1st of September

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Publication Date
Wed Dec 07 2011
Journal Name
Journal Of Planner And Development
centralization and decentralization in Iraq and its future prospects
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Centralization and decentralization, planning and development, and community participation in the management of its affairs and to activate all the abilities that multiple methods aimed at creating the proper environment for the growth and development of society in the place where he lives. As long as the overall trend in Iraq, represented by the Permanent Constitution of decentralization to regions and provinces, the solutions to the obstacles that may face this transition in some respects presents ways of coordination and integration between multiple levels of planning which can be exercised by the schematic in the future the organization. In this paper some of the visions and ideas that can  contribute to the organization

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