Artificial Neural Networks for Renewable Energy Systems and Real-World Applications - Tapa blanda

Acerca de los autores

Ammar Elsheikh received the 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 Tanta University and Tokyo Institute of Technology. He is one of the 2% influential scholars, which depicts the 100,000 top-scientists in the world. His research interests include renewable energy, manufacturing processes, and the application of artificial intelligence techniques in engineering problems.

MOHAMED ABD ELAZIZ received the B.S. and M.S. degrees in Computer science from the Zagazig University, in 2008 and 2011, respectively. He received Ph.D. degree in mathematics and computer science from Zagazig University, Egypt in 2014. From 2008 to 2011, he was Assistant lecturer in Department of computer science. He is Program director of artificial intelligence science at Galala university, Egypt. He is the author of more than 200 articles. ABD ELAZIZ is one of the 2% influential scholars, which depicts the 100,000 top-scientists in the world. His research interests include metaheuristic technique, security IoT, cloud computing machine learning, signal processing, image processing, and evolutionary algorithms.

De la contraportada

Artificial Neural Networks for Renewable Energy Systems and Real-World Applications presents current trends for the solution of complex engineering problems in the application, modelling, analysis, and optimization of different energy systems and manufacturing processes.

The applications of Artificial Neural Networks (ANN) in different engineering disciplines have attracted the attention of researchers to solve complex engineering problems that cannot be solved through conventional methods. 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 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 system and manufacturing process analysis.