Computational Optimal Control: Tools and Practice provides a detailed guide to informed use of computational optimal control in advanced engineering practice, addressing the need for a better understanding of the practical application of optimal control using computational techniques. Throughout the text the authors employ an advanced aeronautical case study to provide a practical, real-life setting for optimal control theory. This case study focuses on an advanced, real-world problem known as the “terminal bunt manoeuvre” or special trajectory shaping of a cruise missile. Representing the many problems involved in flight dynamics, practical control and flight path constraints, this case study offers an excellent illustration of advanced engineering practice using optimal solutions. The book describes in practical detail the real and tested optimal control software, examining the advantages and limitations of the technology. Featuring tutorial insights into computational optimal formulations and an advanced case-study approach to the topic, Computational Optimal Control: Tools and Practice provides an essential handbook for practising engineers and academics interested in practical optimal solutions in engineering. Focuses on an advanced, real-world aeronautical case study examining optimisation of the bunt manoeuvre Covers DIRCOL, NUDOCCCS, PROMIS and SOCS (under the GESOP environment), and BNDSCO Explains how to configure and optimize software to solve complex real-world computational optimal control problems Presents a tutorial three-stage hybrid approach to solving optimal control problem formulations
Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and superconducting waves. This fact is a critical advantage in practical applications in diverse fields of engineering, where signals are routinely analyzed and processed in time/space, frequency, and phase domains. Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely subject. Demonstrating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of: Conventional complex-valued neural networks Quaternionic neural networks Clifford-algebraic neural networks Presented by international experts in the field, Complex-Valued Neural Networks: Advances and Applications is ideal for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians interested in computational intelligence, artificial intelligence, machine learning theories, and algorithms.
The presentation of a novel theory in orthogonal regression The literature about neural-based algorithms is often dedicated to principal component analysis (PCA) and considers minor component analysis (MCA) a mere consequence. Breaking the mold, Neural-Based Orthogonal Data Fitting is the first book to start with the MCA problem and arrive at important conclusions about the PCA problem. The book proposes several neural networks, all endowed with a complete theory that not only explains their behavior, but also compares them with the existing neural and traditional algorithms. EXIN neurons, which are of the authors' invention, are introduced, explained, and analyzed. Further, it studies the algorithms as a differential geometry problem, a dynamic problem, a stochastic problem, and a numerical problem. It demonstrates the novel aspects of its main theory, including its applications in computer vision and linear system identification. The book shows both the derivation of the TLS EXIN from the MCA EXIN and the original derivation, as well as: Shows TLS problems and gives a sketch of their history and applications Presents MCA EXIN and compares it with the other existing approaches Introduces the TLS EXIN neuron and the SCG and BFGS acceleration techniques and compares them with TLS GAO Outlines the GeTLS EXIN theory for generalizing and unifying the regression problems Establishes the GeMCA theory, starting with the identification of GeTLS EXIN as a generalization eigenvalue problem In dealing with mathematical and numerical aspects of EXIN neurons, the book is mainly theoretical. All the algorithms, however, have been used in analyzing real-time problems and show accurate solutions. Neural-Based Orthogonal Data Fitting is useful for statisticians, applied mathematics experts, and engineers.
With 40% new material the new edition of Advanced Wireless Networks provides a comprehensive representation of the key issues in 4G wireless networks. Focussing on cognitive, cooperative and opportunistic paradigms to provide further increase in network efficiency, the book explores and addresses issues in wireless internet, mobile cellular and WLAN, as well as sensor, ad hoc, bio-inspired, active and cognitive networks. It examines the problem of cross-layer optimisation and network information theory as well as adaptability and reconfigurability in wireless networks. This book is an integral description of future wireless networks and the interconnection between their elements. The information is presented in a logical order within each chapter making it ideal for all levels of reader including researchers involved in modelling and analysis of future networks as well as engineers working in the area. Each chapter starts with introductory material and gradually includes more sophisticated models and mathematical tools concluding with a comprehensive list of references. Fully updated throughout with five new chapters on Opportunistic Communications; Relaying and Mesh Networks; Topology Control; Network Optimization; and Cognitive Radio Resource Management Unifies the latest research on cognitive, cooperative and opportunistic paradigms in wireless communications Provides efficient analytical tools for network analysis Discusses security issues, an essential element of working with wireless networks Supports advanced university and training courses in the field Companion website containing extra appendix on Queuing theory
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include: An introduction to Dirichlet Distribution, Exponential Families and their applications. A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods. A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning. All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online. This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology. Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.
On a worldwide basis, the development of SmartGrids is a consistent answer to the problem of an efficient and sustainable delivery of electric energy through distribution grids. SmartGrids are a combination of information and communication technologies and new energy technologies. There are many different definitions of the concept of SmartGrids and thus it appears indispensable to gather the knowledge available from both industry and research laboratories in one book. Distributed generation is rightly receiving an increased amount of attention and will become an integral part of urban energy systems, providing consumers and energy providers with safe, affordable, clean, reliable, flexible and readily-accessible energy services. The aim of this book is to describe future electricity networks that will enable all energy services to become sustainable. The traditional design of network control systems with a centralized structure is not in-line with the paradigm of the unbundled electricity system and decentralized control; this is highlighted by looking at how future active networks will efficiently link small- and medium-scale power sources with consumer demands, allowing decisions to be made on how best to operate in real time. It also looks at the level of control required: power flow assessment, voltage control and protection require cost-competitive technologies and new communication systems with more sensors and actuators than presently used, certainly in relation to the distribution systems. To manage active networks, a vision of grid computing is created that assures universal access to computing resources. An intelligent grid infrastructure gives more flexibility concerning demand and supply, providing new instruments for optimal and cost-effective grid operation at the same time.
A major challenge in constraint programming is to develop efficient generic approaches to solve instances of the constraint satisfaction problem (CSP). With this aim in mind, this book provides an accessible synthesis of the author's research and work in this area, divided into four main topics: representation, inference, search, and learning. The results obtained and reproduced in this book have a wide applicability, regardless of the nature of the problem or the constraints involved, making it an extremely user-friendly resource for those involved in this field.
A unique, hands-on guide to interactive modeling and simulation of engineering systems This book describes advanced, cutting-edge techniques for dynamic system simulation using the DESIRE modeling/simulation software package. It offers detailed guidance on how to implement the software, providing scientists and engineers with powerful tools for creating simulation scenarios and experiments for such dynamic systems as aerospace vehicles, control systems, or biological systems. Along with two new chapters on neural networks, Advanced Dynamic-System Simulation, Second Edition revamps and updates all the material, clarifying explanations and adding many new examples. A bundled CD contains an industrial-strength version of OPEN DESIRE as well as hundreds of program examples that readers can use in their own experiments. The only book on the market to demonstrate model replication and Monte Carlo simulation of real-world engineering systems, this volume: Presents a newly revised systematic procedure for difference-equation modeling Covers runtime vector compilation for fast model replication on a personal computer Discusses parameter-influence studies, introducing very fast vectorized statistics computation Highlights Monte Carlo studies of the effects of noise and manufacturing tolerances for control-system modeling Demonstrates fast, compact vector models of neural networks for control engineering Features vectorized programs for fuzzy-set controllers, partial differential equations, and agro-ecological modeling Advanced Dynamic-System Simulation, Second Edition is a truly useful resource for researchers and design engineers in control and aerospace engineering, ecology, and agricultural planning. It is also an excellent guide for students using DESIRE.
Praise for the First Edition This book is refreshing to read since it takes an important topic… and presents it in a clear and concise manner by using examples that include visual presentations of the problem, solution methods, and results along with an explanation of the mathematical and procedural steps required to model the problem and work through to a solution.” —Journal of Classification Thoroughly updated and revised, Network and Discrete Location: Models, Algorithms, and Applications, Second Edition remains the go-to guide on facility location modeling. The book offers a unique introduction to methodological tools for solving location models and provides insight into when each approach is useful and what information can be obtained. The Second Edition focuses on real-world extensions of the basic models used in locating facilities, including production and distribution systems, location-inventory models, and defender-interdictor problems. A unique taxonomy of location problems and models is also presented. Featuring examples using the author’s own software—SITATION, MOD-DIST, and MENU-OKF—as well as Microsoft Office® Excel®, the book provides: • A theoretical and applied perspective on location models and algorithms • An intuitive presentation of the uses and limits of modeling techniques • An introduction to integrated location-inventory modeling and defender-interdictor models for the design of reliable facility location systems • A full range of exercises to equip readers with an understanding of the basic facility location model types Network and Discrete Location: Models, Algorithms, and Applications, Second Edition is an essential resource for practitioners in applied and discrete mathematics, operations research, industrial engineering, and quantitative geography. The book is also a useful textbook for upper-level undergraduate, graduate, and MBA courses.
Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networks Offers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and opportunistic networks Demonstrates the practices for designing effective protocol/applications for next generation wireless networks Includes case studies showcasing the importance of properly understanding fundamental mobility model properties in wireless network performance evaluation
Use TRIZ to unlock creative problem solving Are you new to TRIZ and looking for an easy-to-follow guide on how you can use it to enhance your company's creativity, innovation and problem-solving abilities? Look no further! Written in plain English and packed with tons of accessible and easy-to-follow instruction, TRIZ For Dummies shows you how to use this powerful toolkit to discover all the ways of solving a problem, uncover new concepts and identify previously unseen routes for new product development. An international science that relies on the study of patterns in problems and solutions, TRIZ offers a powerful problem-solving and creativity-generating solution for companies looking to promote innovation, especially in the face of having to do more with less. Inside, you'll find out how to successfully apply this problem-solving toolkit to benefit from the experience of the whole world—not just the spontaneous and occasional creativity of individuals or groups of engineers with an organisation. Learn to think like a genius with TRIZ Discover the benefits of TRIZ as a tool for businesses Find fun and simple exercises for putting TRIZ into practise Benefit from industry examples of where TRIZ has worked—and how With the help of TRIZ For Dummies, you'll get the skills needed to see the wood for the trees and solve complex problems with creativity, ingenuity and innovation.
A mixture of theory, experiments, and simulations that provide qualitative and quantitative insights into the technology The important new technology of wireless sensor and actuator networks provides radically new communication and networking paradigms with many new applications. Wireless Sensor and Actuator Networks is a timely text that presents a fault-tolerant, reliable, low-latency, and energy-aware framework for wireless sensor and actuator networks, enabling readers to fulfill the ultimate goals of the applications—such as protecting critical infrastructures, achieving timely emergency responses, and monitoring the environment. Taking a problem-oriented approach, this resource discusses a wide range of computing and communication problems and solutions that arise in rapidly emerging wireless sensor and actuator networks, striking a balance between theory and practice. Discusses backbones as subsets of sensors or actuators that suffice for performing basic data communication operations or area sensing coverage Surveys existing data communication schemes (broadcasting, routing, multicasting, anycasting, geocasting) for sensor-actuator coordination Reviews the location service techniques Addresses the problem of energy-efficient data gathering by mobile sinks/actuators Describes protocols for coordination and topology control in sensor, actuator, and robot networks Reviews existing solutions to the sensor placement problem in wireless sensor and actuator networks This book is unique in that it addresses sensor and actuator networking in a comprehensive manner—covering all the aspects and providing up-to-date information—so that industry operators and academics from various areas can learn more about current networking trends, become aware of the possible architectures, and understand the advantages and limits in future commercial, social, and educational applications. Wireless Sensor and Actuator Networks is appropriate for graduate students in computer science, electrical engineering, and telecommunications, as well as practitioners working as engineers, programmers, and technologists.
Optimal Portfolio Modeling is an easily accessible introduction to portfolio modeling for those who prefer an intuitive approach to this discipline. While early chapters provide engaging insights on the statistical properties of markets, this book quickly moves on to illustrate invaluable trading and risk control models based on popular programs such as Excel and the statistical modeling language R. This reliable resource presents modeling formulas that will allow you to effectively maximize the performance, minimize the drawdown, and manage the risk of your portfolio.