Artificial Neural Networks for Renewable Energy Systems and Real-World Applications
106,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 6-10 Tagen
payback
53 °P sammeln
  • Broschiertes Buch

Artificial Neural Networks for Renewable Energy Systems and Real-World Applications presents current trends for the solution of complex engineering problems in the application, modeling, analysis, and optimization of different energy systems and manufacturing processes. With growing research catering to the applications of neural networks in specific industrial applications, this reference provides a single resource catering to a broader perspective of ANN in renewable energy systems and manufacturing processes.
ANN-based methods have attracted the attention of scientists and researchers in…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
Artificial Neural Networks for Renewable Energy Systems and Real-World Applications presents current trends for the solution of complex engineering problems in the application, modeling, analysis, and optimization of different energy systems and manufacturing processes. With growing research catering to the applications of neural networks in specific industrial applications, this reference provides a single resource catering to a broader perspective of ANN in renewable energy systems and manufacturing processes.

ANN-based methods have attracted the attention of scientists and researchers in different engineering and industrial disciplines, making this book a useful reference for all researchers and engineers interested in artificial networks, renewable energy systems, and manufacturing process analysis.
Autorenporträt
Ammar Elsheikh received B.S. and M.S. degrees in mechanical engineering from Tanta University, Tanta, Egypt and Ph.D. degree from Huazhong University of Science and Technology, Wuhan, China. He is currently working as an associative professor in both universities. His research interests include renewable energy, manufacturing systems, and the applications of ANN and metaheuristic techniques in engineering problems.