Stability and Stabilization of BNs with Probabilistic Features

This book studies the stability and stabilization of BNs, which are binary models of complex systems, such as biological networks. BNs face uncertainties and disturbances, such as stochastic impulses, probabilistic switching and random noises, which challenge their analysis and design. We consider BNs that can achieve stability and stabilization with probability one or in probability, and BNs that have randomly missing data in some states. We also cover related topics, such as bisimulation-based stabilization of PBCNs and stabilization of BCNs with stochastic impulses. We present novel methods and results based on algebraic, matrix and probability techniques, and apply them to realistic examples, such as wireless sensor networks, genetic toggle switch and cell cycle models.This book is for researchers, engineers and graduate students who are interested in BNs and their applications. The readers should know linear algebra, matrix theory, set theory and probability theory. The book can also be a reference for related courses or seminars on BNs.