In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is applied to learn the control structure for self-tuning PID type neuro-controller. Where the neural network is used to minimize the error function by adjusting the PID gains. Simulation results show that the self-tuning PID scheme can deal with a large unknown nonlinearity
In this paper, variable gain nonlinear PD and PI fuzzy logic controllers are designed and the effect of the variable gain characteristic of these controllers is analyzed to show its contribution in enhancing the performance of the closed loop system over a conventional linear PID controller. Simulation results and time domain performance characteristics show how these fuzzy controllers outperform the conventional PID controller when used to control a nonlinear plant and a plant that has time delay.
The research aims to identify the correlation between self-reliance and human relationship of kindergartens’ teachers. Total of (120) kindergarten teachers at Baghdad city. To collect needed data, two scales were administered to the research sample consisted of (25) items of each scale with (five) alternatives. The results revealed that teachers have good level of self-reliance and human relationship. There is a positive correlation between self-reliance and human relationship.
Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r
... Show MoreThis paper studies the oscillation properties and asymptotic behavior of all solutions of the 2×2 system of second-order half-linear neutral differential equations. Four results are obtained in this research. The first and second results are auxiliary results while the third and fourth results are main results. All possible cases of non-oscillating bounded solutions for this system are estimated and analyzed. It is noted that the parameters that affect the volatility of the solutions are Qi,Ri on the one hand and r1 and r2 on the other hand. For this purpose, and through investigation, it is shown that there are only fourteen possible cases of non-oscillating bounded solutions for this system, so all these cases must be treated, in the fir
... Show MoreFree Piston Engine Linear Generator (FPELG) is a modern engine and promising power generation engine. It has many advantages compared to conventional engines such as less friction, few numbers of parts, and high thermal efficiency. The cycle-to-cycle variation one of the big challenges of the FPELG because it is influence on the stability and output power of the engine. Therefore, in this study, the effect of ignition time on combustion characteristics is investigated. The single-cylinder FPELG with spark ignition (SI) combustion type by using compressed natural gas (CNG) fuel type was set to run. LabVIEW is used to run the engine and control of input parameters. All experimental data
Surface water flow samples were collected with distances downstream over Saqlawiya main drain whose stretch of about 24.5 km. The drain travels through different land use pattern, before, flowing into Tigris River. Eight sampling points were carefully
selected downstream the channel during dry season. The examined water parameters were pH, NH3, NO3, PO= 4, BOD5, COD, TDS, S.S, Cl-, SO= 4, Na+ , Ca+2, Mg+2, and Oil and Grease. Descriptive and inferential methods through finding the best curve fit correlation were employed in the study to test the strength of the association between water chemical characteristics and distance downstream the channel. A comparison of the values of chemical parameters at the Al-Saqlawiya Drain-Tigris Riv
Rehabilitation robots are widely recognized as vital for restoring motor function in patients with lower-limb impairments. A Modified Fractional-Order Proportional-Integral-Derivative (MFOPID) controller is proposed to improve trajectory tracking of a 2-DoF Lower Limb Rehabilitation Exoskeleton Robot (LLRER). The classical FOPID is augmented with a modified control formulation by which steady-state error is reduced and the transient response is sharpened. Controller gains and fractional orders were tuned offline using a hybrid metaheuristic Improved Elk Herd Optimization hybridized with Grey Wolf and Multi-Verse Optimization algorithms (IElk-GM) so that exploration and exploitation are balanced. Superiority over the classical FOPID
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