Bøker i IEEE Press Series on Systems Science and Engineering-serien

Various systems science and engineering disciplines are covered and challenging new research issues in these disciplines are revealed. They will be extremely valuable for the readers to search for some new research directions and problems.

An authoritative and comprehensive guide to managing energy conservation in infrastructuresEnergy Conservation in Residential, Commercial, and Industrial Facilities offers an essential guide to the business models and engineering design frameworks for the implementation of energy conservation in infrastructures. The presented models of both physical and technological systems can be applied to a wide range of structures such as homes, hotels, public facilities, industrial facilities, transportation, and water/energy supply systems. The authors--noted experts in the field--explore the key performance indicators that are used to evaluate energy conservation strategies and the energy supply scenarios as part of the design and operation of energy systems in infrastructures.The text is based on a systems approach that demonstrates the effective management of building energy knowledge and supports the simulation, evaluation, and optimization of several building energy conservation scenarios. In addition, the authors explore new methods of developing energy semantic network (ESN) superstructures, energy conservation optimization techniques, and risk-based life cycle assessments. This important text:* Defines the most effective ways to model the infrastructure of physical and technological systems* Includes information on the most widely used techniques in the validation and calibration of building energy simulation* Offers a discussion of the sources, quantification, and reduction of uncertainty* Presents a number of efficient energy conservation strategies in infrastructure systems, including HVAC, lighting, appliances, transportation, and industrial facilities* Describes illustrative case studies to demonstrate the proposed energy conservation framework, practices, methods, engineering designs, control, and technologiesWritten for students studying energy conservation as well as engineers designing the next generation of buildings, Energy Conservation in Residential, Commercial, and Industrial Facilities offers a wide-ranging guide to the effective management of energy conservation in infrastructures.

An original, systematic-solution approach to uncertain nonlinear systems control and modeling using fuzzy equations and fuzzy differential equations There are various numerical and analytical approaches to the modeling and control of uncertain nonlinear systems. Fuzzy logic theory is an increasingly popular method used to solve inconvenience problems in nonlinear modeling. Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number presents a structured approach to the control and modeling of uncertain nonlinear systems in industry using fuzzy equations and fuzzy differential equations. The first major work to explore methods based on neural networks and Bernstein neural networks, this innovative volume provides a framework for control and modeling of uncertain nonlinear systems with applications to industry. Readers learn how to use fuzzy techniques to solve scientific and engineering problems and understand intelligent control design and applications. The text assembles the results of four years of research on control of uncertain nonlinear systems with dual fuzzy equations, fuzzy modeling for uncertain nonlinear systems with fuzzy equations, the numerical solution of fuzzy equations with Z-numbers, and the numerical solution of fuzzy differential equations with Z-numbers. Using clear and accessible language to explain concepts and principles applicable to real-world scenarios, this book: Presents the modeling and control of uncertain nonlinear systems with fuzzy equations and fuzzy differential equations Includes an overview of uncertain nonlinear systems for non-specialists Teaches readers to use simulation, modeling and verification skills valuable for scientific research and engineering systems development Reinforces comprehension with illustrations, tables, examples, and simulations Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number is suitable as a textbook for advanced students, academic and industrial researchers, and practitioners in fields of systems engineering, learning control systems, neural networks, computational intelligence, and fuzzy logic control.

E-CARGO and Role-Based CollaborationA model for collaboratively solving complex problemsE-CARGO and Role-Based Collaboration offers a unique guide that explains the nature of collaboration, explores an easy-to-follow process of collaboration, and defines a model to solve complex problems in collaboration and complex systems. Written by a noted expert on the topic, the book initiates the study of an effective collaborative system from a novel perspective. The role-based collaboration (RBC) methodology investigates the most important aspects of a variety of collaborative systems including societal-technical systems. The models and algorithms can also be applied across system engineering, production, and management.The RBC methodology provides insights into complex systems through the use of its core model E-CARGO. The E-CARGO model provides the fundamental components, principles, relationships, and structures for specifying the state, process, and evolution of complex systems. This important book:* Contains a set of concepts, models, and algorithms for the analysis, design, implementation, maintenance, and assessment of a complex system* Presents computational methods that use roles as a primary underlying mechanism to facilitate collaborative activities including role assignment* Explores the RBC methodology that concentrates on the aspects that can be handled by individuals to establish a well-formed team* Offers an authoritative book written by a noted expert on the topicWritten for researchers and practitioners dealing with complex problems in collaboration systems and technologies, E-CARGO and Role-Based Collaboration contains a model to solve real world problems with the help of computer-based systems.

A comprehensive exploration of the control schemes of human-robot interactionsIn Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation.Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control.The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics.Readers will also enjoy:* A thorough introduction to model-based human-robot interaction control* Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles* Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control* In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learningPerfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.