Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal International Journal of Advances in Applied Sciences
Hassan Farahan Rashag
Al- Furat Al-Awsat Technical University
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimization of transmission signal by artificial intelligent Hassan Farahan Rashag; Mohammed H. Ali
International Journal of Advances in Applied Sciences Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (722.509 KB) | DOI: 10.11591/ijaas.v8.i4.pp290-292

Abstract

In this method, radial basis function network RBFNN is an artificial intelligent which is used to identify and classify the communication system performance.  RBFNN is one type of neural network which has activation functions. It consists of three layer input layer, hidden layer and output linear combination. One of the main problems of communication system is that it causes slow response for sending signal via the transmission devices. Therefore, the artificial intelligent by RBFNN is used to optimize the transmission signal. The input signal is trained and testing by neurons with weight and this lead to provide linear output. The simulation results have the optimization specifics over the traditional communication transmission devices.
Improved speed response of DC motor via intelligent techniques Hassan Farahan Rashag
International Journal of Advances in Applied Sciences Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (945.514 KB) | DOI: 10.11591/ijaas.v8.i3.pp204-207

Abstract

The classical Proportional- Integral (PI) control for Direct Current (DC) motor causes slow response of actual speed with high overshoot and undershoot which leads to sluggishness of the system. To minimize the problem of PI controller, intelligent technique based on hybrid neural network sliding mode control NN-SMC is suggested.  The benefits of SMC are that it is simple, and tough to parameter deviations as compared with other controllers. In this paper, the neural network NN is used to minimize the error between reference speed and actual speed. In addition, the SMC aim is to control and optimize the voltage that is supplies the DC motor which guarantees the robust performance of the speed controller under disturbances. The proposed method for the speed control is first calculated and executed to DC motor by using MATLAB SIMULINK. The results of the suggested NN-SMC are compared with the traditional PI controller. The results obviously show the supremacy of NN-SMC over PI controller.
Co-Authors Mohammed H. Ali