Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, as well as research-driven – there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Key features: Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications. Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition. Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms. Accompanied by a website hosting additional material, including the software toolbox and lecture notes. Autonomous Learning Systems provides a ‘one-stop shop’ on the subject for academics, students, researchers and practicing engineers. It is also a valuable reference for Government agencies and software developers.
Керамическое покрытие чаши 13 режимов: рис/крупы; плов/ризотто; разогрев; пароварка/суп; запекание/десерт и др. Система «Fuzzy logic» - автоматическое приготовление риса и круп с помощью специального датчика и запатентованного цикла работы прибора
Tested and proven strategy to develop optimal automated process fault analyzers Process fault analyzers monitor process operations in order to identify the underlying causes of operational problems. Several diagnostic strategies exist for automating process fault analysis; however, automated fault analysis is still not widely used within the processing industries due to problems of cost and performance as well as the difficulty of modeling process behavior at needed levels of detail. In response, this book presents the method of minimal evidence (MOME), a model-based diagnostic strategy that facilitates the development and implementation of optimal automated process fault analyzers. MOME was created at the University of Delaware by the researchers who developed the FALCON system, a real-time, online process fault analyzer. The authors demonstrate how MOME is used to diagnose single and multiple fault situations, determine the strategic placement of process sensors, and distribute fault analyzers within large processing systems. Optimal Automated Process Fault Analysis begins by exploring the need to automate process fault analysis. Next, the book examines: Logic of model-based reasoning as used in MOME MOME logic for performing single and multiple fault diagnoses Fuzzy logic algorithms for automating MOME Distributing process fault analyzers throughout large processing systems Virtual SPC analysis and its use in FALCONEER IV Process state transition logic and its use in FALCONEER IV The book concludes with a summary of the lessons learned by employing FALCONEER IV in actual process applications, including the benefits of «intelligent supervision» of process operations. With this book as their guide, readers have a powerful new tool for ensuring the safety and reliability of any chemical processing system.
БЕЙСБОЛКА CAYLER&SONS 2TONE FUZZY LEO CAP - Оригинальная расцветка - Отличное качество - Размер регулируется - Фирменная нашивка
Объем 4 литра Сенсорные кнопки управления Информативный ЖКИ-дисплей с подсветкой Удобная панель управления на крышке Чаша с двухсторонним антипригарным покрытием 27 программ приготовления Управление Fuzzy Logic 24 часовая отсрочка включения Возможность ручного управления Автоматическое отключение Автоматическая функция подогрева Номинальная мощность 900 Вт 230 B ~ 50 Гц