Bor-Sen Chen – författare

Big Mechanisms in Systems Biology: Big Data Mining, Network Modeling, and Genome-Wide Data Identification explains big mechanisms of systems biology by system identification and big data mining methods using models of biological systems. Systems biology is currently undergoing revolutionary changes in response to the integration of powerful technologies. Faced with a large volume of available literature, complicated mechanisms, small prior knowledge, few classes on the topics, and causal and mechanistic language, this is an ideal resource.

This book addresses system immunity, regulation, infection, aging, evolution, and carcinogenesis, which are complicated biological systems with inconsistent findings in existing resources. These inconsistencies may reflect the underlying biology time-varying systems and signal transduction events that are often context-dependent, which raises a significant problem for mechanistic modeling since it is not clear which genes/proteins to include in models or experimental measurements.

The book is a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in an in-depth understanding on how to process and apply great amounts of biological data to improve research.

Written in a didactic manner in order to explain how to investigate Big Mechanisms by big data mining and system identification Provides more than 140 diagrams to illustrate Big Mechanism in systems biology Presents worked examples in each chapter

Systems Evolutionary Biology: Biological Network Evolution Theory, Stochastic Evolutionary Game Strategies, and Applications to Systems Synthetic Biology discusses the evolutionary game theory and strategies of nonlinear stochastic biological networks under random genetic variations and environmental disturbances and their application to systematic synthetic biology design. The book provides more realistic stochastic biological system models to mimic the real biological systems in evolutionary process and then introduces network evolvability, stochastic evolutionary game theory and strategy based on nonlinear stochastic networks in evolution. Readers will find remarkable, revolutionary information on genetic evolutionary biology that be applied to economics, engineering and bioscience.

Explains network fitness, network evolvability and network robustness of biological networks from the systematic perspective Discusses the evolutionary noncooperative and cooperative game strategies of biological networks Offers detailed diagrams to help readers understand biological networks, their systematic behaviors and the simulational results of evolutionary biological networks Includes examples in every chapter with computational simulation to illustrate the solution procedure of evolutionary theory, strategy and results

Systems Immunology and Infection Microbiology provides a large amount of biological system models, diagrams and flowcharts to illustrate development procedures and help users understand the results of systems immunology and infection microbiology. Chapters discuss systems immunology, systems infection microbiology, systematic inflammation and immune responses in restoration and regeneration process, systems' innate and adaptive immunity in infection process, systematic genetic and epigenetic pathogenic/defensive mechanism during bacterial infection on human cells is introduced, and the systematic genetic and epigenetic pathogenic/defensive mechanisms during viral infection on human cells.

This book provides new big data-driven and systems-driven systems immunology and infection microbiology to researchers applying systems biology and bioinformatics in their work. It is also invaluable to several members of biomedical field who are interested in learning more about those approaches.

Encompasses one applicable example in every chapter to illustrate the solution procedure from big data mining, network modeling, host/pathogen cross-talk detection, drug target identification and systems drug design Presents flowcharts to represent the development procedure of systematic immunology and infection in a very clear format Contains 100 color diagrams to help readers understand the related biological networks, their corresponding mechanisms, and significant network biomarkers for therapeutic drug design

The H∞ control has been one of the important robust control approaches since the 1980s. This book extends the area to nonlinear stochastic H2/H∞ control, and studies more complex and practically useful mixed H2/H∞ controller synthesis rather than the pure H∞ control. Different from the commonly used convex optimization method, this book applies the Nash game approach to give necessary and sufficient conditions for the existence and uniqueness of the mixed H2/H∞ control. Researchers will benefit from our detailed exposition of the stochastic mixed H2/H∞ control theory, while practitioners can apply our efficient algorithms to address their practical problems.

Most systems in science, engineering, and biology are of partial differential systems (PDSs) modeled by partial differential equations. Many books about partial differential equations have been written by mathematicians and mainly address some fundamental mathematic backgrounds and discuss some mathematic properties of partial differential equations. Only a few books on PDSs have been written by engineers; however, these books have focused mainly on the theoretical stabilization analysis of PDSs, especially mechanical systems. This book investigates both robust stabilization control design and robust filter design and reference tracking control design in mechanical, signal processing, and control systems to fill a gap in the study of PDSs.Robust Engineering Designs of Partial Differential Systems and Their Applications offers some fundamental background in the first two chapters. The rest of the chapters focus on a specific design topic with a corresponding deep investigation into robust H∞ filtering, stabilization, or tracking design for more complex and practical PDSs under stochastic fluctuation and external disturbance.This book is aimed at engineers and scientists and addresses the gap between the theoretical stabilization results of PDSs in academic and practical engineering designs more focused on the robust H∞ filtering, stabilization, and tracking control problems of linear and nonlinear PDSs under intrinsic random fluctuation and external disturbance in industrial applications.Part I provides backgrounds on PDSs, such as Galerkin’s, and finite difference methods to approximate PDSs and a fuzzy method to approximate nonlinear PDSs. Part II examines robust H∞ filter designs for the robust state estimation of linear and nonlinear stochastic PDSs. And Part III treats robust H∞ stabilization and tracking control designs of linear and nonlinear PDSs. Every chapter focuses on an engineering design topic with both theoretical design analysis and practical design examples.

Most systems in science, engineering, and biology are of partial differential systems (PDSs) modeled by partial differential equations. Many books about partial differential equations have been written by mathematicians and mainly address some fundamental mathematic backgrounds and discuss some mathematic properties of partial differential equations. Only a few books on PDSs have been written by engineers; however, these books have focused mainly on the theoretical stabilization analysis of PDSs, especially mechanical systems. This book investigates both robust stabilization control design and robust filter design and reference tracking control design in mechanical, signal processing, and control systems to fill a gap in the study of PDSs.Robust Engineering Designs of Partial Differential Systems and Their Applications offers some fundamental background in the first two chapters. The rest of the chapters focus on a specific design topic with a corresponding deep investigation into robust H∞ filtering, stabilization, or tracking design for more complex and practical PDSs under stochastic fluctuation and external disturbance.This book is aimed at engineers and scientists and addresses the gap between the theoretical stabilization results of PDSs in academic and practical engineering designs more focused on the robust H∞ filtering, stabilization, and tracking control problems of linear and nonlinear PDSs under intrinsic random fluctuation and external disturbance in industrial applications.Part I provides backgrounds on PDSs, such as Galerkin’s, and finite difference methods to approximate PDSs and a fuzzy method to approximate nonlinear PDSs. Part II examines robust H∞ filter designs for the robust state estimation of linear and nonlinear stochastic PDSs. And Part III treats robust H∞ stabilization and tracking control designs of linear and nonlinear PDSs. Every chapter focuses on an engineering design topic with both theoretical design analysis and practical design examples.

Game theory involves multi-person decision making and differential dynamic game theory has been widely applied to n-person decision making problems, which are stimulated by a vast number of applications. This book addresses the gap to discuss general stochastic n-person noncooperative and cooperative game theory with wide applications to control systems, signal processing systems, communication systems, managements, financial systems, and biological systems. H8 game strategy, n-person cooperative and noncooperative game strategy are discussed for linear and nonlinear stochastic systems along with some computational algorithms developed to efficiently solve these game strategies.

This book introduces multi-objective design methods to solve multi-objective optimization problems (MOPs) of linear/nonlinear dynamic systems under intrinsic random fluctuation and external disturbance. The MOPs of multiple targets for systems are all transformed into equivalent linear matrix inequality (LMI)-constrained MOPs. Corresponding reverse-order LMI-constrained multi-objective evolution algorithms are introduced to solve LMI-constrained MOPs using MATLAB®. All proposed design methods are based on rigorous theoretical results, and their applications are focused on more practical engineering design examples.Features:Discusses multi-objective optimization from an engineer’s perspectiveContains the theoretical design methods of multi-objective optimization schemesIncludes a wide spectrum of recent research topics in control design, especially for stochastic mean field diffusion problemsCovers practical applications in each chapter, like missile guidance design, economic and financial systems, power control tracking, minimization design in communication, and so forthExplores practical multi-objective optimization design examples in control, signal processing, communication, and cyber-financial systemsThis book is aimed at researchers and graduate students in electrical engineering, control design, and optimization.

This book introduces multi-objective design methods to solve multi-objective optimization problems (MOPs) of linear/nonlinear dynamic systems under intrinsic random fluctuation and external disturbance. The MOPs of multiple targets for systems are all transformed into equivalent linear matrix inequality (LMI)-constrained MOPs. Corresponding reverse-order LMI-constrained multi-objective evolution algorithms are introduced to solve LMI-constrained MOPs using MATLAB®. All proposed design methods are based on rigorous theoretical results, and their applications are focused on more practical engineering design examples.Features:Discusses multi-objective optimization from an engineer’s perspectiveContains the theoretical design methods of multi-objective optimization schemesIncludes a wide spectrum of recent research topics in control design, especially for stochastic mean field diffusion problemsCovers practical applications in each chapter, like missile guidance design, economic and financial systems, power control tracking, minimization design in communication, and so forthExplores practical multi-objective optimization design examples in control, signal processing, communication, and cyber-financial systemsThis book is aimed at researchers and graduate students in electrical engineering, control design, and optimization.

This book introduces the centralized robust H∞ team formation tracking control strategy of multi-unmanned aerial vehicle (multi-UAV) network system under intrinsic random fluctuation, time-varying delay and packet dropout in wireless communication, and external disturbance. A simple robust decentralized H∞ proportional-integral-derivative (PID) reference tracking network control strategy is introduced for practical applications of team formation of large-scale UAV under control saturation constraint, external disturbance, and vortex coupling. It provides practical design procedures based on linear matrix inequalities (LMIs) solvable via LMI TOOLBOX in MATLAB®. Features: Focuses on the stabilization of a QUAV under finite-time switching model control (SMC) Discusses robustness control design for formation tracking in UAV networks Introduces different robust centralized and decentralized H∞ attack-tolerant observer-based reference team formation tracking control of large-scale UAVs Reviews practical case studies in each chapter to introduce the design procedures Includes design examples of team formation of 25 quadrotor UAVs and a team formation example of five hybrid quadrotor/biped robot sub-teamsThis book is aimed at researchers and graduate students in control and electrical engineering.

This book introduces a robust H∞ physics-generated AI-driven filter and controller, along with a nonlinear Luenberger observer model and a state estimation error dynamic model, to effectively address HJIEs for robust H∞ state estimation (filtering) and reference trajectory tracking control in nonlinear stochastic systems. Additionally, it presents a method for training deep neural networks (DNNs) using these models, alongside a physics-generated AI-driven observer-based reference tracking control scheme, with applications in the guidance and control of relevant systems.Key features:Provides theoretical analysis and detailed design procedure for physics-generated AI-driven H∞ or mixed H2/H∞ filterApplies physics-generated AI-driven robust H∞ or mixed H2/H∞ filter and reference tracking control schemes to the trajectory estimation and reference tracking control of man-made machinesIntroduces physics-generated AI-driven decentralized H∞ observer-based team formation tracking control of large-scale quadrotor UAVs, biped robots or LEO satellitesPromulgates the idea of the forthcoming age of physics-generated AI in robotDescribes robust physics-generated AI-driven filter and control schemes for complex man-made machinesThis book is aimed at graduate students and researchers in control science, signal processing and artificial intelligence.

Game theory involves multi-person decision making and differential dynamic game theory has been widely applied to n-person decision making problems, which are stimulated by a vast number of applications. This book addresses the gap to discuss general stochastic n-person noncooperative and cooperative game theory with wide applications to control systems, signal processing systems, communication systems, managements, financial systems, and biological systems. H∞ game strategy, n-person cooperative and noncooperative game strategy are discussed for linear and nonlinear stochastic systems along with some computational algorithms developed to efficiently solve these game strategies.

The H∞ control has been one of the important robust control approaches since the 1980s. This book extends the area to nonlinear stochastic H2/H∞ control, and studies more complex and practically useful mixed H2/H∞ controller synthesis rather than the pure H∞ control. Different from the commonly used convex optimization method, this book applies the Nash game approach to give necessary and sufficient conditions for the existence and uniqueness of the mixed H2/H∞ control. Researchers will benefit from our detailed exposition of the stochastic mixed H2/H∞ control theory, while practitioners can apply our efficient algorithms to address their practical problems.