Engineering practice often has to deal with complex systems of multiple variable and multiple parameter models almost always with strong non-linear coupling. The conventional analytical techniques-based approaches for describing and predicting the behaviour of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. These approaches normally are too categorical in the sense that in the name of “modelling accuracy” they try to describe all the structural details of the real physical system to be modelled. This can significantly increase the intricacy of the model and may result in a enormous computational burden without achieving considerable improvement of the solution. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction, the more work has to be invested to obtain it. A further important component of machine intelligence is a kind of “structural uniformity” giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to the ready-to-wear industry, which introduced products, which can be slightly modified later on in contrast to tailor-made creations aiming at maximum accuracy from the beginning. These subsequent corrections can be carried out by machines automatically. This “learning ability” is a key element of machine intelligence. The past decade confirmed that the view of typical components of the present soft computing as fuzzy logic, neural computing, evolutionary computation and probabilistic reasoning are of complementary nature and that the best results can be applied by their combined application. Today, the two complementary branches of Machine Intelligence, that is, Artificial Intelligence and Computational Intelligence serve as the basis of Intelligent Engineering Systems. The huge number of scientific results published in Journal and conference proceedings worldwide substantiates this statement. The present book contains several articles taking different viewpoints in the field of intelligent systems.

A classic and influential work that laid the theoretical foundations for information theory and a timely text for contemporary informations theorists and practitioners. With the influential book Cybernetics, first published in 1948, Norbert Wiener laid the theoretical foundations for the multidisciplinary field of cybernetics, the study of controlling the flow of information in systems with feedback loops, be they biological, mechanical, cognitive, or social. At the core of Wiener's theory is the message (information), sent and responded to (feedback); the functionality of a machine, organism, or society depends on the quality of messages. Information corrupted by noise prevents homeostasis, or equilibrium. And yet Cybernetics is as philosophical as it is technical, with the first chapter devoted to Newtonian and Bergsonian time and the philosophical mixed with the technical throughout. This book brings the 1961 second edition back into print, with new forewords by Doug Hill and Sanjoy Mitter. Contemporary readers of Cybernetics will marvel at Wiener's prescience—his warnings against “noise,” his disdain for “hucksters” and “gadget worshipers,” and his view of the mass media as the single greatest anti-homeostatic force in society. This edition of Cybernetics gives a new generation access to a classic text.

Agricultural systems are uniquely complex systems, given that agricultural systems are parts of natural and ecological systems. Those aspects bring in a substantial degree of uncertainty in system operation. Also, impact factors, such as weather factors, are critical in agricultural systems but these factors are uncontrollable in system management. Modern agriculture has been evolving through precision agriculture beginning in the late 1980s and biotechnological innovations in the early 2000s. Precision agriculture implements site-specific crop production management by integrating agricultural mechanization and information technology in geographic information system (GIS), global navigation satellite system (GNSS), and remote sensing. Now, precision agriculture is set to evolve into smart agriculture with advanced systematization, informatization, intelligence and automation. From precision agriculture to smart agriculture, there is a substantial amount of specific control and communication problems that have been investigated and will continue to be studied. In this book, the core ideas and methods from control problems in agricultural production systems are extracted, and a system view of agricultural production is formulated for the analysis and design of management strategies to control and optimize agricultural production systems while exploiting the intrinsic feedback information-exchanging mechanisms. On this basis, the theoretical framework of agricultural cybernetics is established to predict and control the behavior of agricultural production systems through control theory.

Systems, cybernetics, control, and automation (SCCA) are four interrelated and overlapping scientific and technological fields that have contributed substantially to the development, growth, and progress of human society. A large number of models, methods, and tools were developed that assure high efficiency of SCCA applied to practical situations. The real-life applications of SCCA encompass a wide range of man-made or biological systems, including transportations, power generation, chemical industry, robotics, manufacturing, cybernetics organisms (cyborgs), aviation, economic systems, enterprise, systems, medical/health systems, environmental applications, and so on. The SCCA fields exhibit strong influences on society and rise, during their use and application, many ethical concerns and dilemmas. This book provides a consolidated and concise overview of SCCA, in a single volume for the first time, focusing on ontological, epistemological, social impact, ethical, and general philosophical issues. It is appropriate for use in engineering courses as a convenient tutorial source providing fundamental conceptual and educational material on these issues, or for independent reading by students and scientists. Included in the book is: Background material on philosophy and systems theoryMajor ontological, epistemological, societal and ethical/philosophical aspects of the four fields that are considered in the bookOver 400 references and a list of 130 additional books in the relevant fields Over 100 colored photos and 70 line figures that illustrate the text