Ahmad Taher Azar

TL;DR: The acquired results demonstrate that the hybrid bijectives soft set neural network method can deliver expressively more accurate and consistent predictive accuracy than well-known algorithms such as bijective soft set classifier, back propagation network, multi-layered perceptron, decision table and naive Bayes classification algorithms.

TL;DR: In this paper, a closed-loop output voltage control system for an energy source based on a fuel cell linked to a DC voltage generator is proposed, which is based on the design of a fractional controller to regulate the output voltage of the converter for distinct resistive load levels.

TL;DR: A novel technique for predicting equilibrated urea concentration and PDUR in the form of a Takagi-Sugeno-Kang fuzzy inference system is presented and a comparative analysis suggests that the proposed modelling approach outperforms other traditional urea kinetic models.

TL;DR: A comparative analysis for two methods used to obtain the inverse kinematics of a 3-RRR manipulator using an adaptive neuro-fuzzy inference structure (AFNIS) model, which is compared with a traditional neural network model for the same manipulator in order to ascertain which model is better in angles prediction, training time and overall performance.

TL;DR: This chapter announces a new four-dimensional hyperchaotic system having two positive Lyapunov exponents, a zero Lyap Unov exponent, and a negative Lyap unov exponent to validate the theoretical model.