Author: IEEE Staff
Publisher:
ISBN: 9781728160795
Category :
Languages : en
Pages :
Get Book Here
Book Description
The ICoIAS 2020 will provide a premier interdisciplinary platform for scientists, researchers, engineers, practitioners and educators to present and discuss the most recent innovations, trends and concerns as well as practical challenges encountered and solutions adopted in the field of IAS
Author: Zhong-Ping Jiang
Publisher: Now Publishers
ISBN: 9781680837520
Category : Technology & Engineering
Languages : en
Pages : 122
Get Book Here
Book Description
The recent success of Reinforcement Learning and related methods can be attributed to several key factors. First, it is driven by reward signals obtained through the interaction with the environment. Second, it is closely related to the human learning behavior. Third, it has a solid mathematical foundation. Nonetheless, conventional Reinforcement Learning theory exhibits some shortcomings particularly in a continuous environment or in considering the stability and robustness of the controlled process. In this monograph, the authors build on Reinforcement Learning to present a learning-based approach for controlling dynamical systems from real-time data and review some major developments in this relatively young field. In doing so the authors develop a framework for learning-based control theory that shows how to learn directly suboptimal controllers from input-output data. There are three main challenges on the development of learning-based control. First, there is a need to generalize existing recursive methods. Second, as a fundamental difference between learning-based control and Reinforcement Learning, stability and robustness are important issues that must be addressed for the safety-critical engineering systems such as self-driving cars. Third, data efficiency of Reinforcement Learning algorithms need be addressed for safety-critical engineering systems. This monograph provides the reader with an accessible primer on a new direction in control theory still in its infancy, namely Learning-Based Control Theory, that is closely tied to the literature of safe Reinforcement Learning and Adaptive Dynamic Programming.
Author: IEEE Staff
Publisher:
ISBN: 9781728141435
Category :
Languages : en
Pages :
Get Book Here
Book Description
ic ETITE 20 expresses its concern towards the upgrading of research in Information Technology and Engineering It motivates to provide a worldwide platform to researchers far and widespread by exploring their innovations in the field of science and technology The mission is to promote and improve the research and development related to the topics of the conference The essential objective of the conference is to assist the researchers in discovering the global linkage for future joint efforts in their academic outlook
Author: IEEE Staff
Publisher:
ISBN: 9781509036189
Category :
Languages : en
Pages :
Get Book Here
Book Description
Big Data overall architecture consists of three layers data storage, data processing and data analysis Data storage layer stores complex type and mass data, data processing layer realizes real time processing of massive data, and only through data analysis layer, smart, in depth and valuable information are got When talking about big data, it comes to the first is 4V characteristics of big data, namely Volumes, Variety, Velocity, Veracity Big data processing key technology generally includes data acquisition, data preprocessing, data storage and data management, data analysis and mining, big show and application (big data retrieval, data visualization, big data applications, data security, etc ) In recent years, Big Data has become a new ubiquitous term Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself 2017 2nd IEEE International Conference on Big Data Analysis (ICBDA 2017) provides a leading forum for diss
Author: Vivienne Sze
Publisher: Springer Nature
ISBN: 3031017668
Category : Technology & Engineering
Languages : en
Pages : 254
Get Book Here
Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Author: IEEE Staff
Publisher:
ISBN: 9781509066261
Category :
Languages : en
Pages :
Get Book Here
Book Description
Process & Device Technologies 1 VLSI Design & Circuits 2 Analog, Mixed Signal and RF Circuits 3 Application Specific SOCs 4 Circuits and Systems for Wireless Communications 5 Testing, Reliability, Fault Tolerance 6 Advanced Memory 7 FPGA 8 Circuits Simulation, Synthesis, Varification and Physical Design 9 CAD for System, DFM & Testing 10 MEMS Techniques 11 Nanoelectronics and Gigascale Systems 12 New Devices Hetrojunction Devices, Fin FET, CNT MTJ Devices, 3D Integration, etc 13 Advanced Interconnection Technology, High K Metal gate technology and other VLSI New Processing, New technologies 14 VLSI application for energy generation, conservation and control 15 Processing, Devices Modeling & Simulation 16 Other VLSI Devices and Design related topics
Author: Madan Gupta
Publisher: John Wiley & Sons
ISBN: 0471460923
Category : Computers
Languages : en
Pages : 752
Get Book Here
Book Description
Neuronale Netze haben sich in vielen Bereichen der Informatik und künstlichen Intelligenz, der Robotik, Prozeßsteuerung und Entscheidungsfindung bewährt. Um solche Netze für immer komplexere Aufgaben entwickeln zu können, benötigen Sie solide Kenntnisse der Theorie statischer und dynamischer neuronaler Netze. Aneignen können Sie sie sich mit diesem Lehrbuch! Alle theoretischen Konzepte sind in anschaulicher Weise mit praktischen Anwendungen verknüpft. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen.
Author: Long Jin
Publisher: Frontiers Media SA
ISBN: 2832552013
Category : Science
Languages : en
Pages : 301
Get Book Here
Book Description
Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.
Author: IEEE Staff
Publisher:
ISBN: 9781538637890
Category :
Languages : en
Pages :
Get Book Here
Book Description
ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields
Author: Cornelius T. Leondes
Publisher: Elsevier
ISBN: 0080498981
Category : Computers
Languages : en
Pages : 485
Get Book Here
Book Description
This volume is the first diverse and comprehensive treatment of algorithms and architectures for the realization of neural network systems. It presents techniques and diverse methods in numerous areas of this broad subject. The book covers major neural network systems structures for achieving effective systems, and illustrates them with examples. This volume includes Radial Basis Function networks, the Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold Networks, weight initialization, fast and efficient variants of Hamming and Hopfield neural networks, discrete time synchronous multilevel neural systems with reduced VLSI demands, probabilistic design techniques, time-based techniques, techniques for reducing physical realization requirements, and applications to finite constraint problems. A unique and comprehensive reference for a broad array of algorithms and architectures, this book will be of use to practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering. - Radial Basis Function networks - The Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold Networks - Weight initialization - Fast and efficient variants of Hamming and Hopfield neural networks - Discrete time synchronous multilevel neural systems with reduced VLSI demands - Probabilistic design techniques - Time-based techniques - Techniques for reducing physical realization requirements - Applications to finite constraint problems - Practical realization methods for Hebbian type associative memory systems - Parallel self-organizing hierarchical neural network systems - Dynamics of networks of biological neurons for utilization in computational neuroscience
