Grey system theory is a multidisciplinary scientific approach, which deals with systems that have partially unknown information (small sample and uncertain information). Grey modeling as an important component of such theory gives successful results with limited amount of data. Grey Models are divided into two types; univariate and multivariate grey models. The univariate grey model with one order derivative equation GM (1,1) is the base stone of the theory, it is considered the time series prediction model but it doesn’t take the relative factors in account. The traditional multivariate grey models GM(1,M) takes those factor in account but it has a complex structure and some defects in " modeling mechanism", "parameter estimation "and "model structure", So that traditional GM(1,M) submitted to many trials of optimizations to getting rid this defects. This research shows the characteristics of the traditional GM(1,M), the problems it suffer from, the method of getting rid of such problems and presents two optimized multivariable grey model of one order derivative equation. the first one is called the Optimized Grey Model abbreviated as OGM(1, M) by adding the linear correction term h1(M-1)and the grey action quantity term (h2) to the traditional model GM(1,M) the latter is called Optimized Background value Grey Model OBGM(1,M) by optimizing the Background value of the last model OGM(1,M). We use two A realistic data represents the water consumption in Baghdad at the period (2016-2022) to compare the two optimized models with the traditional represents the water consumption in Baghdad at the period (2016-2022)). we use the mean absolute percentage error (MAPE) and the determination coefficient R2. To compare the two optimized model with traditional one. The results show that the two optimized have less values than the those of the traditional model GM(I,M), and that verify the correctness of defects analysis of GM(1,M).
A theoretical calculation of the binding and excitation energies have been used at low – lying energies based on shell model and quantum theory. In this model, we evaluated the energies under assume Ni 28 56 30 as inert core with two nucleon extra, nucleons in the 2P3/2 , 1f 5/2 and 2P1/2 configuration. Modified Surface Delta Interaction (MSDI) and Reid's Potential (RP) theory for two body matrix elements are evaluated by using a Matlab program to calculate the energies of experimental and Reid single particle energies. Our results of the theoretical calculation have been compared with the experimental results, which show no good agreement with the experiment but have a good agreement wit
... Show MoreThese days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that. The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the proce
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In the current work various types of epoxy composites were added to concrete to enhance its effectiveness as a gamma- ray shield. Four epoxy samples of (E/clay/B4C) S1, (E/Mag/B4C) S2, (EPIL) S3 and (Ep) S4 were used in a comparative study of gamma radiation attenuation properties of these shields that calculating using Mont Carlo code (MCNP-5). Adopting Win X-com software and Artificial Neural Network (ANN), µ/ρ revealed great compliance with MCNP-5. By applying (µ/ρ) output for gamma at different energies, HVL, TVL and MFP have been also estimated. ANN technique was simulated to estimate (µ/ρ) and dose rates. According to the results, µ/ρ of all epoxy samples scored higher than standard concrete. Both S2 and S3 samples having h
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As one type of resistance furnace, the electrical tube furnace (ETF) typically experiences input noise, measurement noise, system uncertainties, unmodeled dynamics and external disturbances, which significantly degrade its temperature control performance. To provide precise, and robust temperature tracking performance for the ETF, a robust composite control (RCC) method is proposed in this paper. The overall RCC method consists of four elements: First, the mathematical model of the ETF system is deduced, then a state feedback control (SFC) is constructed. Third, a novel disturbance observer (DO) is designed to estimate the lumped disturbance with one observer parameter. Moreover, the stability of the closed loop system including controller
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Abstract
The research to have a clear perceptions about the knowledge value added to assess the knowledge resources of the Iraqi private banks, depending on the value added methodology of the proposed defined (Housel & Bell, 2001), which assumes that the knowledge value added come through synergetic relationship between knowledge resource and information technology, trying to the possibility of mainstream theory and its application in the Iraqi environment and interpretation of results, and on this basis was launched search of a research problem took root synergetic nature of the relationship between knowledge (human) resource and
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Abstract
The research aims to identify the nature of the relationship between the level of debt used and economic value added of companies listed on the Iraq Stock Exchange under the Contrast sizes of these companies , The research addressed the theoretical concepts associated with each of the debt financing , economic added value and Organization size With the use of financial techniques in the practical side to measure these variables, The research community Represent of the shareholding companies listed on the Iraq Stock Exchange with a choice of intentionally sample of 24 joint stock companies representing 
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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
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The effect of superficial gas velocity within the range 0.01-0.164 m/s on gas holdup (overall, riser and down comer), volumetric oxygen mass transfer coefficient, liquid circulation velocity was studied in an internal loop concentric tubes airlift reactor (working volume 45 liters). It was shown that as the usg increases the gas holdup and also the liquid circulation velocity increase. Also it was found that increasing superficial gas velocity lead to increase the interfacial area that increases the overall oxygen mass transfer coefficient. The hydrodynamic experimental results were modeled with the available equations in the literature. The predicted data gave an acceptable accuracy with the empirical data.
The final
... Show MoreIn this study, we investigate the behavior of the estimated spectral density function of stationary time series in the case of missing values, which are generated by the second order Autoregressive (AR (2)) model, when the error term for the AR(2) model has many of continuous distributions. The Classical and Lomb periodograms used to study the behavior of the estimated spectral density function by using the simulation.
Abstract
This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control per
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