Zhong Li – författare

This volume presents the proceedings of the Seventh International Colloquium on Finite or Infinite Dimensional Complex Analysis held in Fukuoka, Japan. The contributions offer multiple perspectives and numerous research examples on complex variables, Clifford algebra variables, hyperfunctions and numerical analysis.

This volume presents the proceedings of the Seventh International Colloquium on Finite or Infinite Dimensional Complex Analysis held in Fukuoka, Japan. The contributions offer multiple perspectives and numerous research examples on complex variables, Clifford algebra variables, hyperfunctions and numerical analysis.

This volume presents the proceedings of the Seventh International Colloquium on Finite or Infinite Dimensional Complex Analysis held in Fukuoka, Japan. The contributions offer multiple perspectives and numerous research examples on complex variables, Clifford algebra variables, hyperfunctions and numerical analysis.

This book provides readers with a unified, forward-looking framework for understanding, designing, and optimizing monograph multi-energy systems that link electricity, heat, gas, and hydrogen. Rather than offering a descriptive overview of existing technologies, this book emphasizes theoretical rigor and methodological innovation, providing engineers, researchers, and system planners with the analytical tools and design principles necessary to develop intelligent, adaptive, and resilient microgrid architectures under complex and uncertain environments.Structured into four cohesive parts—Preliminaries, Architecture, Control, and Applications—the book integrates foundational theory with engineering practice. It elaborates on hierarchical and distributed control structures, introduces adaptive dynamic programming (ADP) and learning-based optimization frameworks, and elucidates the role of digital intelligence in enhancing real-time decision-making and operational robustness. Comprehensive case studies drawn from industrial parks, islanded systems, and transportation energy networks demonstrate practical approaches toward achieving low-carbon, efficient, and resilient energy integration.This book serves as both a scholarly reference and a methodological guide for advancing intelligent control, coordination, and optimization in next-generation sustainable energy infrastructures.

This book provides readers with a unified, forward-looking framework for understanding, designing, and optimizing monograph multi-energy systems that link electricity, heat, gas, and hydrogen. Rather than offering a descriptive overview of existing technologies, this book emphasizes theoretical rigor and methodological innovation, providing engineers, researchers, and system planners with the analytical tools and design principles necessary to develop intelligent, adaptive, and resilient microgrid architectures under complex and uncertain environments.Structured into four cohesive parts—Preliminaries, Architecture, Control, and Applications—the book integrates foundational theory with engineering practice. It elaborates on hierarchical and distributed control structures, introduces adaptive dynamic programming (ADP) and learning-based optimization frameworks, and elucidates the role of digital intelligence in enhancing real-time decision-making and operational robustness. Comprehensive case studies drawn from industrial parks, islanded systems, and transportation energy networks demonstrate practical approaches toward achieving low-carbon, efficient, and resilient energy integration.This book serves as both a scholarly reference and a methodological guide for advancing intelligent control, coordination, and optimization in next-generation sustainable energy infrastructures.

This book focuses on modelling and simulation, control and optimization, signal processing, and forecasting in selected nonlinear dynamical systems, presenting both literature reviews and novel concepts. It develops analytical or numerical approaches, which are simple to use, robust, stable, flexible and universally applicable to the analysis of complex nonlinear dynamical systems. As such it addresses key challenges are addressed, e.g. efficient handling of time-varying dynamics, efficient design, faster numerical computations, robustness, stability and convergence of algorithms. The book provides a series of contributions discussing either the design or analysis of complex systems in sciences and engineering, and the concepts developed involve nonlinear dynamics, synchronization, optimization, machine learning, and forecasting. Both theoretical and practical aspects of diverse areas are investigated, specifically neurocomputing, transportation engineering, theoretical electrical engineering, signal processing, communications engineering, and computational intelligence. It is a valuable resource for students and researchers interested in nonlinear dynamics and synchronization with applications in selected areas.

This book focuses on modelling and simulation, control and optimization, signal processing, and forecasting in selected nonlinear dynamical systems, presenting both literature reviews and novel concepts. It develops analytical or numerical approaches, which are simple to use, robust, stable, flexible and universally applicable to the analysis of complex nonlinear dynamical systems. As such it addresses key challenges are addressed, e.g. efficient handling of time-varying dynamics, efficient design, faster numerical computations, robustness, stability and convergence of algorithms. The book provides a series of contributions discussing either the design or analysis of complex systems in sciences and engineering, and the concepts developed involve nonlinear dynamics, synchronization, optimization, machine learning, and forecasting. Both theoretical and practical aspects of diverse areas are investigated, specifically neurocomputing, transportation engineering, theoretical electrical engineering, signal processing, communications engineering, and computational intelligence. It is a valuable resource for students and researchers interested in nonlinear dynamics and synchronization with applications in selected areas.

Research and application of impedance network converters are very popular in recent years, but it still lacks of understanding of and guidelines of impedance networks application, therefore, there is quiet a large potential market about impedance networks converters. This book can serve as a teaching material for graduates and guidelines for engineers as designing an impedance source converter.The main purpose of this book is to understand impedance networks of nonlinear switch circuits and impedance networks matching, which will further put forward understanding of all power converters in view of impedance networks. Taking the impedance network matchings into account leads to a set of criteria for designing an impedance source converter, which is to replace the traditional tedious, manual and experience-dependent design methods.

Research and application of impedance network converters are very popular in recent years, but it still lacks of understanding of and guidelines of impedance networks application, therefore, there is quiet a large potential market about impedance networks converters. This book can serve as a teaching material for graduates and guidelines for engineers as designing an impedance source converter.The main purpose of this book is to understand impedance networks of nonlinear switch circuits and impedance networks matching, which will further put forward understanding of all power converters in view of impedance networks. Taking the impedance network matchings into account leads to a set of criteria for designing an impedance source converter, which is to replace the traditional tedious, manual and experience-dependent design methods.

This book focuses on modelling and simulation, control and optimization, signal processing, and forecasting in selected nonlinear dynamical systems, presenting both literature reviews and novel concepts. It develops analytical or numerical approaches, which are simple to use, robust, stable, flexible and universally applicable to the analysis of complex nonlinear dynamical systems. As such it addresses key challenges are addressed, e.g. efficient handling of time-varying dynamics, efficient design, faster numerical computations, robustness, stability and convergence of algorithms. The book provides a series of contributions discussing either the design or analysis of complex systems in sciences and engineering, and the concepts developed involve nonlinear dynamics, synchronization, optimization, machine learning, and forecasting. Both theoretical and practical aspects of diverse areas are investigated, specifically neurocomputing, transportation engineering, theoretical electrical engineering, signal processing, communications engineering, and computational intelligence. It is a valuable resource for students and researchers interested in nonlinear dynamics and synchronization with applications in selected areas.

Research and application of impedance network converters are very popular in recent years, but it still lacks of understanding of and guidelines of impedance networks application, therefore, there is quiet a large potential market about impedance networks converters.

Bridges corrosion science and artificial intelligence to advance durable, high-performance marine steels Understanding and controlling the corrosion of steels in marine environments is a critical challenge for modern engineering, with far-reaching implications for safety, durability, and sustainability. Marine corrosion not only threatens the integrity of infrastructure such as bridges, offshore platforms, and railways but also leads to significant economic losses and environmental risks. Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions provides a comprehensive exploration of the mechanistic processes underlying steel degradation and introduces innovative, data-driven approaches to improve corrosion resistance. Offering valuable insights into stress corrosion cracking, fatigue, and general degradation mechanisms in harsh marine conditions, the book systematically investigates corrosion behavior across a range of engineering steels, including high-manganese steel, titanium-steel composites, low-alloy rebar, and ductile iron. Beyond mechanistic analysis, dedicated chapters highlight pioneering applications of big data and artificial intelligence, such as predictive modeling, image recognition for pit analysis, and IoT-enabled real-time monitoring. These AI-driven approaches enable researchers and engineers to accelerate alloy design, optimize material selection, and implement proactive maintenance strategies. Combining deep scientific understanding with cutting-edge computational tools, Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions: Demonstrates how corrosion science is evolving toward intelligent, sustainable solutions for the most demanding industrial applicationsExplores alloying strategies and material innovations to enhance corrosion resistanceIntroduces novel electrochemical evaluation methods and image-based corrosion quantificationPresents real-world case studies, including corrosion fatigue in railway components and offshore steel performanceDiscusses multi-modal frameworks combining physics, machine learning, and computer visionOffers forward-looking insights on AI-driven alloy design for sustainability and cost reductionConnecting fundamental research to practical engineering solutions across multiple industries, Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions is an essential resource for graduate-level courses in materials science, corrosion engineering, and mechanical engineering, particularly within programs in metallurgy, chemical engineering, and civil infrastructure. It is also a valuable reference for engineers, corrosion specialists, and researchers working in marine, aerospace, and energy industries.

Bridges corrosion science and artificial intelligence to advance durable, high-performance marine steels Understanding and controlling the corrosion of steels in marine environments is a critical challenge for modern engineering, with far-reaching implications for safety, durability, and sustainability. Marine corrosion not only threatens the integrity of infrastructure such as bridges, offshore platforms, and railways but also leads to significant economic losses and environmental risks. Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions provides a comprehensive exploration of the mechanistic processes underlying steel degradation and introduces innovative, data-driven approaches to improve corrosion resistance. Offering valuable insights into stress corrosion cracking, fatigue, and general degradation mechanisms in harsh marine conditions, the book systematically investigates corrosion behavior across a range of engineering steels, including high-manganese steel, titanium-steel composites, low-alloy rebar, and ductile iron. Beyond mechanistic analysis, dedicated chapters highlight pioneering applications of big data and artificial intelligence, such as predictive modeling, image recognition for pit analysis, and IoT-enabled real-time monitoring. These AI-driven approaches enable researchers and engineers to accelerate alloy design, optimize material selection, and implement proactive maintenance strategies. Combining deep scientific understanding with cutting-edge computational tools, Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions: Demonstrates how corrosion science is evolving toward intelligent, sustainable solutions for the most demanding industrial applicationsExplores alloying strategies and material innovations to enhance corrosion resistanceIntroduces novel electrochemical evaluation methods and image-based corrosion quantificationPresents real-world case studies, including corrosion fatigue in railway components and offshore steel performanceDiscusses multi-modal frameworks combining physics, machine learning, and computer visionOffers forward-looking insights on AI-driven alloy design for sustainability and cost reduction Connecting fundamental research to practical engineering solutions across multiple industries, Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions is an essential resource for graduate-level courses in materials science, corrosion engineering, and mechanical engineering, particularly within programs in metallurgy, chemical engineering, and civil infrastructure. It is also a valuable reference for engineers, corrosion specialists, and researchers working in marine, aerospace, and energy industries.

Bridges corrosion science and artificial intelligence to advance durable, high-performance marine steels Understanding and controlling the corrosion of steels in marine environments is a critical challenge for modern engineering, with far-reaching implications for safety, durability, and sustainability. Marine corrosion not only threatens the integrity of infrastructure such as bridges, offshore platforms, and railways but also leads to significant economic losses and environmental risks. Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions provides a comprehensive exploration of the mechanistic processes underlying steel degradation and introduces innovative, data-driven approaches to improve corrosion resistance. Offering valuable insights into stress corrosion cracking, fatigue, and general degradation mechanisms in harsh marine conditions, the book systematically investigates corrosion behavior across a range of engineering steels, including high-manganese steel, titanium-steel composites, low-alloy rebar, and ductile iron. Beyond mechanistic analysis, dedicated chapters highlight pioneering applications of big data and artificial intelligence, such as predictive modeling, image recognition for pit analysis, and IoT-enabled real-time monitoring. These AI-driven approaches enable researchers and engineers to accelerate alloy design, optimize material selection, and implement proactive maintenance strategies. Combining deep scientific understanding with cutting-edge computational tools, Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions: Demonstrates how corrosion science is evolving toward intelligent, sustainable solutions for the most demanding industrial applicationsExplores alloying strategies and material innovations to enhance corrosion resistanceIntroduces novel electrochemical evaluation methods and image-based corrosion quantificationPresents real-world case studies, including corrosion fatigue in railway components and offshore steel performanceDiscusses multi-modal frameworks combining physics, machine learning, and computer visionOffers forward-looking insights on AI-driven alloy design for sustainability and cost reduction Connecting fundamental research to practical engineering solutions across multiple industries, Marine Corrosion of Steels:Mechanisms and AI-Driven Solutions is an essential resource for graduate-level courses in materials science, corrosion engineering, and mechanical engineering, particularly within programs in metallurgy, chemical engineering, and civil infrastructure. It is also a valuable reference for engineers, corrosion specialists, and researchers working in marine, aerospace, and energy industries.

The 1960s were perhaps a decade of confusion, when scientists faced d- culties in dealing with imprecise information and complex dynamics. A new set theory and then an in?nite-valued logic of Lot? A. Zadeh were so c- fusing that they were called fuzzy set theory and fuzzy logic; a deterministic system found by E. N. Lorenz to have random behaviours was so unusual that it was lately named a chaotic system. Just like irrational and imaginary numbers, negative energy, anti-matter, etc., fuzzy logic and chaos were gr- ually and eventually accepted by many, if not all, scientists and engineers as fundamental concepts, theories, as well as technologies. In particular, fuzzy systems technology has achieved its maturity with widespread applications in many industrial, commercial, and technical ?elds, ranging from control, automation, and arti?cial intelligence to image/signal processing,patternrecognition,andelectroniccommerce.Chaos,ontheother hand,wasconsideredoneofthethreemonumentaldiscoveriesofthetwentieth century together with the theory of relativity and quantum mechanics. As a very special nonlinear dynamical phenomenon, chaos has reached its current outstanding status from being merely a scienti?c curiosity in the mid-1960s to an applicable technology in the late 1990s. Finding the intrinsic relation between fuzzy logic and chaos theory is certainlyofsigni?cantinterestandofpotentialimportance.Thepast20years have indeed witnessed some serious explorations of the interactions between fuzzylogicandchaostheory,leadingtosuchresearchtopicsasfuzzymodeling of chaotic systems using Takagi–Sugeno models, linguistic descriptions of chaotic systems, fuzzy control of chaos, and a combination of fuzzy control technology and chaos theory for various engineering practices.

The 1960s were perhaps a decade of confusion, when scientists faced d- culties in dealing with imprecise information and complex dynamics. A new set theory and then an in?nite-valued logic of Lot? A. Zadeh were so c- fusing that they were called fuzzy set theory and fuzzy logic; a deterministic system found by E. N. Lorenz to have random behaviours was so unusual that it was lately named a chaotic system. Just like irrational and imaginary numbers, negative energy, anti-matter, etc., fuzzy logic and chaos were gr- ually and eventually accepted by many, if not all, scientists and engineers as fundamental concepts, theories, as well as technologies. In particular, fuzzy systems technology has achieved its maturity with widespread applications in many industrial, commercial, and technical ?elds, ranging from control, automation, and arti?cial intelligence to image/signal processing,patternrecognition,andelectroniccommerce.Chaos,ontheother hand,wasconsideredoneofthethreemonumentaldiscoveriesofthetwentieth century together with the theory of relativity and quantum mechanics. As a very special nonlinear dynamical phenomenon, chaos has reached its current outstanding status from being merely a scienti?c curiosity in the mid-1960s to an applicable technology in the late 1990s. Finding the intrinsic relation between fuzzy logic and chaos theory is certainlyofsigni?cantinterestandofpotentialimportance.Thepast20years have indeed witnessed some serious explorations of the interactions between fuzzylogicandchaostheory,leadingtosuchresearchtopicsasfuzzymodeling of chaotic systems using Takagi–Sugeno models, linguistic descriptions of chaotic systems, fuzzy control of chaos, and a combination of fuzzy control technology and chaos theory for various engineering practices.

Bringing together the two seemingly unrelated concepts,fuzzy logic andchaos theory,isprimarilymotivatedbytheconceptofsoft computing (SC),initiated by Lot? A. Zadeh, the founder of fuzzy set theory. The principal constituents of SC are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming parts of belief networks, genetic algorithms, chaos theory and learning theory. What is important to note is that SC is not a melange of FL, NN and PR. Rather, it is an integration in which each of the partners contributes a distinct methodology for addressing problems in their common domain. In this perspective, the principal cont- butions of FL, NN and PR are complementary rather than competitive. SC di?ers from conventional (hard) computing in that it is tolerant of imprecision, uncertainty and partial truth. In e?ect, the role model for soft computing is the human mind. From the general SC concept, we extract FL and chaos theory as the object of this book to study their relationships or interactions. Over the past few decades, fuzzy systems technology and chaos theory have received ever increasing research interests from, respectively, systems and control engineers, theoretical and experimental physicists, applied ma- ematicians, physiologists, and other communities of researchers. Especially, as one of the emerging information processing technologies, fuzzy systems technology has achieved widespread applications around the globe in many industriesandtechnical?elds,rangingfromcontrol,automation,andarti?cial intelligence (AI) to image/signal processing and pattern recognition. On the otherhand,inengineeringsystemschaostheoryhasevolvedfrombeingsimply a curious phenomenon to one with real, practical signi?cance andutilization.

Bringing together the two seemingly unrelated concepts,fuzzy logic andchaos theory,isprimarilymotivatedbytheconceptofsoft computing (SC),initiated by Lot? A. Zadeh, the founder of fuzzy set theory. The principal constituents of SC are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming parts of belief networks, genetic algorithms, chaos theory and learning theory. What is important to note is that SC is not a melange of FL, NN and PR. Rather, it is an integration in which each of the partners contributes a distinct methodology for addressing problems in their common domain. In this perspective, the principal cont- butions of FL, NN and PR are complementary rather than competitive. SC di?ers from conventional (hard) computing in that it is tolerant of imprecision, uncertainty and partial truth. In e?ect, the role model for soft computing is the human mind. From the general SC concept, we extract FL and chaos theory as the object of this book to study their relationships or interactions. Over the past few decades, fuzzy systems technology and chaos theory have received ever increasing research interests from, respectively, systems and control engineers, theoretical and experimental physicists, applied ma- ematicians, physiologists, and other communities of researchers. Especially, as one of the emerging information processing technologies, fuzzy systems technology has achieved widespread applications around the globe in many industriesandtechnical?elds,rangingfromcontrol,automation,andarti?cial intelligence (AI) to image/signal processing and pattern recognition. On the otherhand,inengineeringsystemschaostheoryhasevolvedfrombeingsimply a curious phenomenon to one with real, practical signi?cance andutilization.

In essence, the dynamics of real world systems (i.e. engineered systems, natural systems, social systesms, etc.) is nonlinear. The analysis of this nonlinear character is generally performed through both observational and modeling processes aiming at deriving appropriate models (mathematical, logical, graphical, etc.) to simulate or mimic the spatiotemporal dynamics of the given systems. The complex intrinsic nature of these systems (i.e. nonlinearity and spatiotemporal dynamics) can lead to striking dynamical behaviors such as regular or irregular, stable or unstable, periodicity or multi-periodicity, torus or chaotic dynamics. The various potential applications of the knowledge about such dynamics in technical sciences (engineering) are being intensively demonstrated by diverse ongoing research activities worldwide. However, both the modeling and the control of the nonlinear dynamics in a range of systems is still not yet well-understood (e.g. system models with time varying coefficients, immune systems, swarm intelligent systems, chaotic and fractal systems, stochastic systems, self-organized systems, etc.). This is due amongst others to the challenging task of establishing a precise and systematic fundamental or theoretical framework (e.g. methods and tools) to analyze, understand, explain and predict the nonlinear dynamical behavior of these systems, in some cases even in real-time. The full insight in systems’ nonlinear dynamic behavior is generally achieved through approaches involving analytical, numerical and/or experimental methods.

In essence, the dynamics of real world systems (i.e. engineered systems, natural systems, social systesms, etc.) is nonlinear. The analysis of this nonlinear character is generally performed through both observational and modeling processes aiming at deriving appropriate models (mathematical, logical, graphical, etc.) to simulate or mimic the spatiotemporal dynamics of the given systems. The complex intrinsic nature of these systems (i.e. nonlinearity and spatiotemporal dynamics) can lead to striking dynamical behaviors such as regular or irregular, stable or unstable, periodicity or multi-periodicity, torus or chaotic dynamics. The various potential applications of the knowledge about such dynamics in technical sciences (engineering) are being intensively demonstrated by diverse ongoing research activities worldwide. However, both the modeling and the control of the nonlinear dynamics in a range of systems is still not yet well-understood (e.g. system models with time varying coefficients, immune systems, swarm intelligent systems, chaotic and fractal systems, stochastic systems, self-organized systems, etc.). This is due amongst others to the challenging task of establishing a precise and systematic fundamental or theoretical framework (e.g. methods and tools) to analyze, understand, explain and predict the nonlinear dynamical behavior of these systems, in some cases even in real-time. The full insight in systems’ nonlinear dynamic behavior is generally achieved through approaches involving analytical, numerical and/or experimental methods.

The 1960s were perhaps a decade of confusion, when scientists faced d- culties in dealing with imprecise information and complex dynamics. A new set theory and then an in?nite-valued logic of Lot? A. Zadeh were so c- fusing that they were called fuzzy set theory and fuzzy logic; a deterministic system found by E. N. Lorenz to have random behaviours was so unusual that it was lately named a chaotic system. Just like irrational and imaginary numbers, negative energy, anti-matter, etc., fuzzy logic and chaos were gr- ually and eventually accepted by many, if not all, scientists and engineers as fundamental concepts, theories, as well as technologies. In particular, fuzzy systems technology has achieved its maturity with widespread applications in many industrial, commercial, and technical ?elds, ranging from control, automation, and arti?cial intelligence to image/signal processing,patternrecognition,andelectroniccommerce.Chaos,ontheother hand,wasconsideredoneofthethreemonumentaldiscoveriesofthetwentieth century together with the theory of relativity and quantum mechanics. As a very special nonlinear dynamical phenomenon, chaos has reached its current outstanding status from being merely a scienti?c curiosity in the mid-1960s to an applicable technology in the late 1990s. Finding the intrinsic relation between fuzzy logic and chaos theory is certainlyofsigni?cantinterestandofpotentialimportance.Thepast20years have indeed witnessed some serious explorations of the interactions between fuzzylogicandchaostheory,leadingtosuchresearchtopicsasfuzzymodeling of chaotic systems using Takagi–Sugeno models, linguistic descriptions of chaotic systems, fuzzy control of chaos, and a combination of fuzzy control technology and chaos theory for various engineering practices.

Bringing together the two seemingly unrelated concepts,fuzzy logic andchaos theory,isprimarilymotivatedbytheconceptofsoft computing (SC),initiated by Lot? A. Zadeh, the founder of fuzzy set theory. The principal constituents of SC are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming parts of belief networks, genetic algorithms, chaos theory and learning theory. What is important to note is that SC is not a melange of FL, NN and PR. Rather, it is an integration in which each of the partners contributes a distinct methodology for addressing problems in their common domain. In this perspective, the principal cont- butions of FL, NN and PR are complementary rather than competitive. SC di?ers from conventional (hard) computing in that it is tolerant of imprecision, uncertainty and partial truth. In e?ect, the role model for soft computing is the human mind. From the general SC concept, we extract FL and chaos theory as the object of this book to study their relationships or interactions. Over the past few decades, fuzzy systems technology and chaos theory have received ever increasing research interests from, respectively, systems and control engineers, theoretical and experimental physicists, applied ma- ematicians, physiologists, and other communities of researchers. Especially, as one of the emerging information processing technologies, fuzzy systems technology has achieved widespread applications around the globe in many industriesandtechnical?elds,rangingfromcontrol,automation,andarti?cial intelligence (AI) to image/signal processing and pattern recognition. On the otherhand,inengineeringsystemschaostheoryhasevolvedfrombeingsimply a curious phenomenon to one with real, practical signi?cance andutilization.

In essence, the dynamics of real world systems (i.e. engineered systems, natural systems, social systesms, etc.) is nonlinear. The analysis of this nonlinear character is generally performed through both observational and modeling processes aiming at deriving appropriate models (mathematical, logical, graphical, etc.) to simulate or mimic the spatiotemporal dynamics of the given systems. The complex intrinsic nature of these systems (i.e. nonlinearity and spatiotemporal dynamics) can lead to striking dynamical behaviors such as regular or irregular, stable or unstable, periodicity or multi-periodicity, torus or chaotic dynamics. The various potential applications of the knowledge about such dynamics in technical sciences (engineering) are being intensively demonstrated by diverse ongoing research activities worldwide. However, both the modeling and the control of the nonlinear dynamics in a range of systems is still not yet well-understood (e.g. system models with time varying coefficients, immune systems, swarm intelligent systems, chaotic and fractal systems, stochastic systems, self-organized systems, etc.). This is due amongst others to the challenging task of establishing a precise and systematic fundamental or theoretical framework (e.g. methods and tools) to analyze, understand, explain and predict the nonlinear dynamical behavior of these systems, in some cases even in real-time. The full insight in systems’ nonlinear dynamic behavior is generally achieved through approaches involving analytical, numerical and/or experimental methods.

This book contains a collection of recent advanced contributions in the field of nonlinear dynamics and synchronization, including selected applications in the area of theoretical electrical engineering. The present book is divided into twenty-one chapters grouped in five parts. The first part focuses on theoretical issues related to chaos and synchronization and their potential applications in mechanics, transportation, communication and security. The second part handles dynamic systems modelling and simulation with special applications to real physical systems and phenomena. The third part discusses some fundamentals of electromagnetics (EM) and addresses the modelling and simulation in some real physical electromagnetic scenarios. The fourth part mainly addresses stability concerns. Finally, the last part assembles some sample applications in the area of optimization, data mining, pattern recognition and image processing.

This book contains a collection of recent advanced contributions in the field of nonlinear dynamics and synchronization, including selected applications in the area of theoretical electrical engineering. The present book is divided into twenty-one chapters grouped in five parts. The first part focuses on theoretical issues related to chaos and synchronization and their potential applications in mechanics, transportation, communication and security. The second part handles dynamic systems modelling and simulation with special applications to real physical systems and phenomena. The third part discusses some fundamentals of electromagnetics (EM) and addresses the modelling and simulation in some real physical electromagnetic scenarios. The fourth part mainly addresses stability concerns. Finally, the last part assembles some sample applications in the area of optimization, data mining, pattern recognition and image processing.

Presenting the proceedings of the conference held in 1990, this book is aimed at pure mathematicians.

FLINS, an acronym introduced in 1994 and originally for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended into a well-established international research forum to advance the foundations and applications of computational intelligence for applied research in general and for complex engineering and decision support systems.The principal mission of FLINS is bridging the gap between machine intelligence and real complex systems via joint research between universities and international research institutions, encouraging interdisciplinary research and bringing multidiscipline researchers together.FLINS 2020 is the fourteenth in a series of conferences on computational intelligence systems.