The chief objective of Electric Machinery continues to be to build a strong foundation in the basic principles of electromechanics and electric machinery. Through all of its editions, the emphasis of Electric Machinery has been on both physical insight and analytical techniques. Mastery of the material covered will provide both the basis for understanding many real-world electric-machinery APPlications as well as the foundation for proceeding on to more advanced courses in electric machinery design and control.
標(biāo)簽： Machinery Electric 6th ed
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
Control systems are becoming more important every day. At the beginning, the in- dustry used sequential controls for solving a lot of industrial APPlications in control systems, and then the linear systems gave us a huge increase in applying automatic linear control on industrial application. One of the most recent methods for control- ling industrial APPlications is intelligent control, which is based on human behavior or concerning natural process.
標(biāo)簽： Intelligent Control Systems LabVIEW with
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
The countless technological advances of the twentieth century require that futureengineering educationemphasizebridging thegapbetweentheoryand the real world.Thisedition hasbeenprepared withparticular attentiontothe needs of undergraduates, especially those who seek a solid foundation in control theory aswellas an ability tobridgethe gapbetween control theory and itsreal- world APPlications.To help the reader achieve this goal, computer-aided design accuracy checks (CADAC) are used throughout the text to encourage good habits of computerliteracy.Each CADAC uses fundamentalconcepts to ensure the viability of a computer solution.
標(biāo)簽： Analysis Control Linear Design System Fifth and
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
Despite the development of a now vast body of knowledge known as modern control theory, and despite some spectacular APPlications of this theory to practical situations, it is quite clear that much of the theory has yet to find application, and many practical control problems have yet to find a theory which will successfully deal with them. No book of course can remedy the situation at this time. But the aim of this book is to construct one of many bridges that are still required for the student and practicing control engineer between the familiar classical control results and those of modern control theory.
標(biāo)簽： Control Optimal Linear
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
The main aim of this book is to present a unified, systematic description of basic and advanced problems, methods and algorithms of the modern con- trol theory considered as a foundation for the design of computer control and management systems. The scope of the book differs considerably from the topics of classical traditional control theory mainly oriented to the needs of automatic control of technical devices and technological proc- esses. Taking into account a variety of new APPlications, the book presents a compact and uniform description containing traditional analysis and op- timization problems for control systems as well as control problems with non-probabilistic models of uncertainty, problems of learning, intelligent, knowledge-based and operation systems – important for APPlications in the control of manufacturing processes, in the project management and in the control of computer systems.
標(biāo)簽： Modern_Control_Theory
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
If you are acquainted with neural networks, automatic control problems are good industrial APPlications and have a dynamic or evolutionary nature lacking in static pattern-recognition; control ideas are also prevalent in the study of the natural neural networks found in animals and human beings. If you are interested in the practice and theory of control, artificial neu- ral networks offer a way to synthesize nonlinear controllers, filters, state observers and system identifiers using a parallel method of computation.
標(biāo)簽： Control Systems Neural For
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
Despite the development of a now vast body of knowledge known as modern control theory, and despite some spectacular APPlications of this theory to practical situations, it is quite clear that some of the theory has yet to find application, and many practical control problems have yet to find a theory that will successfully deal with them. No one book, of course, can remedy the situation. The aim of this book is to construct bridges that are still required for the student and practicing control engineer between the familiar classical control results and those of modern control theory.
標(biāo)簽： Quadratic Optimal Control Methods Linear
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
Recent years have seen a rapid development of neural network control tech- niques and their successful APPlications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control APPlications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings.
標(biāo)簽： Stable_adaptive_neural_network_co ntrol
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
n recent years, there have been many books published on power system optimization. Most of these books do not cover APPlications of artifi cial intelligence based methods. Moreover, with the recent increase of artifi cial intelligence APPlications in various fi elds, it is becoming a new trend in solving optimization problems in engineering in general due to its advantages of being simple and effi cient in tackling complex problems. For this reason, the application of artifi cial intelligence in power systems has attracted the interest of many researchers around the world during the last two decades. This book is a result of our effort to provide information on the latest APPlications of artifi cial intelligence to optimization problems in power systems before and after deregulation.
標(biāo)簽： Intelligence Artificial System Power in
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation. Similarly, new models based on kernels have had significant impact on both algorithms and APPlications.
標(biāo)簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
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