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Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems.A Bradford Book. Neural Network Modeling and Connectionism series
From foundations to state-of-the-art; the tools and philosophy you need to build network models.
The respected industry standard for technicians working in live entertainment.
From the researcher who was one of the first to identify and analyze the infamous industrial control system malware "Stuxnet," comes a book that takes a new, radical approach to making Industrial control systems safe from such cyber attacks: design the controls systems themselves to be "robust." Other security experts advocate risk management, implementing more firewalls and carefully managing passwords and access. Not so this book: those measures, while necessary, can still be circumvented. Instead, this book shows in clear, concise detail how a system that has been set up with an eye toward quality design in the first place is much more likely to remain secure and less vulnerable to hacking, sabotage or malicious control. It blends several well-established concepts and methods from control theory, systems theory, cybernetics and quality engineering to create the ideal protected system. The book's maxim is taken from the famous quality engineer William Edwards Deming, "If I had to reduce my message to management to just a few words, I'd say it all has to do with reducing variation." Highlights include: - An overview of the problem of "cyber fragility" in industrial control systems - How to make an industrial control system "robust," including principal design objectives and overall strategic planning - Why using the methods of quality engineering like the Taguchi method, SOP and UML will help to design more "armored" industrial control systems.
Control networks span a wide range of application areas. These networks are put into action in the `Digital Home', industrial applications, commercial buildings, transportation systems, gas stations, security systems, and they are found in most instances where smart sensors and smart actuators are used to exchange information. The authors of this volume provide an overview of various control network protocols and discuss LonTalk® protocol, Neuron® chip, programming model, network structures, network management, interoperability between nodes, application profiles, development and maintenance tools, performance analysis, and standardization activities. Open Control Networks: LonWorks/EIA 709 Technology will be an important resource for advanced students of control systems and embedded systems, engineers designing distributed networks, systems designers and architects, and others developing smart buildings and intelligent transportation systems.
Analysis and Control of Boolean Networks presents a systematic new approach to the investigation of Boolean control networks. The fundamental tool in this approach is a novel matrix product called the semi-tensor product (STP). Using the STP, a logical function can be expressed as a conventional discrete-time linear system. In the light of this linear expression, certain major issues concerning Boolean network topology – fixed points, cycles, transient times and basins of attractors – can be easily revealed by a set of formulae. This framework renders the state-space approach to dynamic control systems applicable to Boolean control networks. The bilinear-systemic representation of a Boolean control network makes it possible to investigate basic control problems including controllability, observability, stabilization, disturbance decoupling etc.
Introduction to Show Networking covers the basics of how Ethernet networks provide a platform for entertainment control and audio/video media distribution for concerts, theatre productions, corporate and special events, cruise ship revues, wrestling shows, houses of worship, museum presentations, fountain spectaculars—any kind of show presented live for an audience. The book’s bottom-up approach was designed with show technicians in mind, starting with the basics and then moving up through cables, network switches, and layering, and on through Ethernet, and network components like TCP, UDP, IP and subnet masks, all with a practical focus. More advanced concepts are introduced, including broadcast storms and VLANs, along with show networking best practices. Closing out the book is a network design process demonstrated through practical, real-world examples for lighting, sound, video, scenic automation, and show control networks. An appendix covering binary and hexadecimal numbers is also included. This easy-reading book draws from Huntington’s Show Networks and Control Systems, the industry standard since 1994, but is completely re-focused, reorganized, and updated.
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.
As the sophistication of cyber-attacks increases, understanding how to defend critical infrastructure systems—energy production, water, gas, and other vital systems—becomes more important, and heavily mandated. Industrial Network Security, Second Edition arms you with the knowledge you need to understand the vulnerabilities of these distributed supervisory and control systems. The book examines the unique protocols and applications that are the foundation of industrial control systems, and provides clear guidelines for their protection. This how-to guide gives you thorough understanding of the unique challenges facing critical infrastructures, new guidelines and security measures for critical infrastructure protection, knowledge of new and evolving security tools, and pointers on SCADA protocols and security implementation. - All-new real-world examples of attacks against control systems, and more diagrams of systems - Expanded coverage of protocols such as 61850, Ethernet/IP, CIP, ISA-99, and the evolution to IEC62443 - Expanded coverage of Smart Grid security - New coverage of signature-based detection, exploit-based vs. vulnerability-based detection, and signature reverse engineering