Amirhosein Mosavi

TL;DR: A brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art is considered and the survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems.

TL;DR: An artificial neural network is implemented for predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions and these algorithms can be considered as an exceptional tool for predicting Thermal conductivity.

TL;DR: In this article, a deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. And the proposed method is applied for modeling both solar panels and wind turbines.

TL;DR: A novel control approach is proposed for a class of uncertain nonlinear system with unmodeled dynamics based on stability analysis of the fractional-order systems based on the linear matrix inequality approach.

TL;DR: The results showed a higher rate of heat rejection from the nan ofluid cooled heat sink compared with water, and the enhancement in performance was analyzed with the help of a temperature difference of nanofluid outlet temperature and water outlet temperature under similar operating conditions.