Enhancement of motor speed identification using artificial neural networks

Arshad B. Salih (Northern Technical University)
Zuhair Shakor Mahmood (Northern Technical University)
Ardm Haseeb Mohammed Ali (Imam Ja’afar Al-Sadiq University)
Ali Najdet Nasret (Northern Technical University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 27, No 3: September 2022

In this study have been utilized a modified version of ant colony optimization to improve the thresholds of neural networks and weights by including therank-weight approach. Furthermore, this technique easily overcome the drawbacks speed up convergence into the minimum while training the backpropagation neural network. The improved ant colony optimization-backpropagation neural.not only has the capacity to map extensively, but it also enhances operating efficiency noticeably, according to the simulation findings. The simulation results revealed that the speed sensor replaced with the ant colony optimization rw-optimized back propagation neural network-speed identification and motor’s speed determined using this approach the result is satisfactory.