Artificial Neural Networks (PDF)

IVAN NUNES DA SILVA was born in São José do Rio Preto, Brazil, in 1967. He graduated in computer science and electrical engineering at the Federal University of Uberlândia, Brazil, in 1991 and 1992, respectively. He received both M.Sc. and Ph.D. degrees in electrical engineering from the State University of Campinas (UNICAMP), Brazil, in 1995 and 1997, respectively. Currently, he is an Associate Professor at the University of São Paulo (USP). His research interests are within the fields of artificial neural networks, fuzzy inference systems, power system automation and robotics. He is also associate editor of the International Journal on Power System Optimization and Editor-in-Chief of the Journal of Control, Automation and Electrical Systems. He has published more than 400 papers in congress proceedings, international journals and book chapters.



DANILO HERNANE SPATTI was born in Araras, Brazil, in 1981. He graduated in electrical engineering from the São Paulo State University (UNESP), Brazil, in 2005. He received both M.Sc. and Ph.D. degrees in electrical engineering from the University of São Paulo (USP), Brazil, in 2007 and 2009, respectively. Currently, he is a Senior Researcher at the University of São Paulo. His research interests are artificial neural networks, computation complexity, systems optimization and intelligent systems.






ROGÉRIO ANDRADE FLAUZINO was born in Franca, Brazil, in 1978. He graduated in electrical engineering and also received M.Sc. degree in electrical engineering from the São Paulo State University (UNESP), Brazil, in 2001 and 2004, respectively. He received Ph.D. degree in electrical engineering from the University of São Paulo (USP), Brazil, in 2007. Currently, he is an Associate Professor at the University of São Paulo. His research interests are artificial neural networks, computational intelligence, fuzzy inference systems and power systems.






SILAS FRANCO DOS REIS ALVES

“The book under review is quite unique, covering many important topics usually omitted from introductory courses on artificial neural networks, and as such it is a valuable reference. … A major advantage of this volume is the interesting choice of examples used, most of which are not commonly considered in the artificial neural network literature.” (Sandro Skansi, Mathematical Reviews, April, 2018)

“This book would be very good for advanced undergraduate students, first-year graduate students, or for anyone wishing to learn about neural networks on their own. It was originally published in Brazil in Portuguese. … The exercises thoroughly test the readers’ understanding of the descriptive material. The practical examples address the training and use of the architecture in the chapter.” (Anthony J. Duben, Computing Reviews, April, 2017)