This book presents advanced methods for the electronic modeling of dynamical systems governed by ordinary differential equations.
It offers a comprehensive toolkit and practical solutions for specialists in nonlinear dynamics who seek experimental validation of their mathematical models. The aim is to empower readers without an extensive background in electronics or circuit theory to translate their theoretical concepts into real-world devices, facilitating the rapid experimental confirmation of numerical and theoretical findings. For experts in electronic engineering, the book showcases how a wide range of non-electronic systems and their unique characteristics can be effectively modeled using electronic circuits.
Bridging the gap between theory and practice, the book serves as a valuable resource on electronics for theorists and mathematicians, and on nonlinear dynamics for experimentalists and engineers. Its audience includes a broad spectrum of readers, from students and engineers to scientists and researchers across various fields.
Jadual kandungan
1 Continuous Dynamical System.- 2 Electronic Modelling.- 3 Analog Modelling and Signal Processing.- 4 Special Issues of Electronic Modelling.- 5 Dynamical Systems and the Corresponding Electronic Models.
Mengenai Pengarang
Vladimir Semenov has two Ph D-degrees: Ph D-degree in Radiophysics and Electronics earned in Russia, Saratov State University and Ph D-degree in Optics earned in France, institute FEMTO-ST. He is a head of the laboratory of computer modelling in electronics created in Saratov State University since 2021.
His research interests includes various phenomena (mostly stochastic ones) in nonlinear single oscillators as well as in ensembles and networks of coupled oscillators: stochastic bifurcations, noise-induced transitions, stochastic resonance, coherence resonance, stochastic control, pattern formation in the dynamics of time-delay oscillators, bistability, chimera states, coarsening, effects in memristor-based oscillators and ensembles, photonic neural networks, spin-networks
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