71,95 €
inkl. MwSt.
Sofort per Download lieferbar
  • Format: PDF

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory.
It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

Produktbeschreibung
This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory.

It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

Autorenporträt
Dr. Gerasimos Rigatos received his Ph.D. from the Dept. of Electrical and Computer Engineering of the National Technical University of Athens, Greece. He had a postdoctoral position at IRISA, Rennes, France, he was an invited professor at the Université Paris XI (Institut d'Eléctronique Fondamentale) and a lecturer in the Dept. of Engineering of Harper-Adams University College, UK. He is now a researcher in the Unit of Industrial Automation, Industrial Systems Institute, Patras, Greece. His research interests include computational intelligence, adaptive systems, mechatronics, robotics and control, optimization and fault diagnosis.
Rezensionen
"Several chapters deal with standard questions like control, synchronization, and estimation. Rigatos uses a clever linearization technique, and then applies variants of linear control techniques to solve these problems for nonlinear models. ... I recommend this book to those interested in neural nets who won't be put off by the density of the mathematics." (Paul Cull, Computing Reviews, December, 2014)