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Author: John Choma Publisher: World Scientific ISBN: 9810227701 Category : Technology & Engineering Languages : en Pages : 886
Book Description
This book addresses the theoretical and practical circuit and system concepts that underpin the design of reliable and reproducible, high performance, monolithic feedback circuits. It is intended for practicing electronics engineers and students who wish to acquire an insightful understanding of the ways in which open loop topologies, closed loop architectures, and fundamental circuit theoretic issues combine to determine the limits of performance of analog networks. Since many of the problems that underpin high speed digital circuit design are a subset of the analysis and design dilemmas confronted by wideband analog circuit designers, the book is also germane to high performance digital circuit design.
Author: John Choma Publisher: World Scientific ISBN: 9810227701 Category : Technology & Engineering Languages : en Pages : 886
Book Description
This book addresses the theoretical and practical circuit and system concepts that underpin the design of reliable and reproducible, high performance, monolithic feedback circuits. It is intended for practicing electronics engineers and students who wish to acquire an insightful understanding of the ways in which open loop topologies, closed loop architectures, and fundamental circuit theoretic issues combine to determine the limits of performance of analog networks. Since many of the problems that underpin high speed digital circuit design are a subset of the analysis and design dilemmas confronted by wideband analog circuit designers, the book is also germane to high performance digital circuit design.
Author: Colin Fyfe Publisher: Springer Science & Business Media ISBN: 1846281180 Category : Computers Languages : en Pages : 388
Book Description
This book is the outcome of a decade’s research into a speci?c architecture and associated learning mechanism for an arti?cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning. The research began with my own thesis at the University of Strathclyde, Scotland, under Professor Douglas McGregor which culminated with me being awarded a PhD in 1995 [52], the title of which was “Negative Feedback as an Organising Principle for Arti?cial Neural Networks”. Naturally enough, having established this theme, when I began to sup- vise PhD students of my own, we continued to develop this concept and this book owes much to the research and theses of these students at the Applied Computational Intelligence Research Unit in the University of Paisley. Thus we discuss work from • Dr. Darryl Charles [24] in Chapter 5. • Dr. Stephen McGlinchey [127] in Chapter 7. • Dr. Donald MacDonald [121] in Chapters 6 and 8. • Dr. Emilio Corchado [29] in Chapter 8. We brie?y discuss one simulation from the thesis of Dr. Mark Girolami [58] in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form [59]. We also must credit Cesar Garcia Osorio, a current PhD student, for the comparative study of the two Exploratory Projection Pursuit networks in Chapter 8. All of Chapters 3 to 8 deal with single stream arti?cial neural networks.
Author: John Choma Publisher: World Scientific Publishing Company ISBN: 981310306X Category : Computers Languages : en Pages : 886
Book Description
This book addresses the theoretical and practical circuit and system concepts that underpin the design of reliable and reproducible, high performance, monolithic feedback circuits. It is intended for practicing electronics engineers and students who wish to acquire an insightful understanding of the ways in which open loop topologies, closed loop architectures, and fundamental circuit theoretic issues combine to determine the limits of performance of analog networks. Since many of the problems that underpin high speed digital circuit design are a subset of the analysis and design dilemmas confronted by wideband analog circuit designers, the book is also germane to high performance digital circuit design.
Author: Giuseppe Ciaburro Publisher: Packt Publishing Ltd ISBN: 1788399412 Category : Computers Languages : en Pages : 264
Book Description
Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.
Author: Karl Johan Åström Publisher: Princeton University Press ISBN: 069121347X Category : Technology & Engineering Languages : en Pages :
Book Description
The essential introduction to the principles and applications of feedback systems—now fully revised and expanded This textbook covers the mathematics needed to model, analyze, and design feedback systems. Now more user-friendly than ever, this revised and expanded edition of Feedback Systems is a one-volume resource for students and researchers in mathematics and engineering. It has applications across a range of disciplines that utilize feedback in physical, biological, information, and economic systems. Karl Åström and Richard Murray use techniques from physics, computer science, and operations research to introduce control-oriented modeling. They begin with state space tools for analysis and design, including stability of solutions, Lyapunov functions, reachability, state feedback observability, and estimators. The matrix exponential plays a central role in the analysis of linear control systems, allowing a concise development of many of the key concepts for this class of models. Åström and Murray then develop and explain tools in the frequency domain, including transfer functions, Nyquist analysis, PID control, frequency domain design, and robustness. Features a new chapter on design principles and tools, illustrating the types of problems that can be solved using feedback Includes a new chapter on fundamental limits and new material on the Routh-Hurwitz criterion and root locus plots Provides exercises at the end of every chapter Comes with an electronic solutions manual An ideal textbook for undergraduate and graduate students Indispensable for researchers seeking a self-contained resource on control theory
Author: Publisher: IOS Press ISBN: Category : Languages : en Pages : 4947
Author: Puyin Liu Publisher: World Scientific ISBN: 9812387862 Category : Computers Languages : en Pages : 395
Book Description
This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to help the reader grasp the underlying theory. This is a valuable reference for scientists and engineers working in mathematics, computer science, control or other fields related to information processing. It can also be used as a textbook for graduate courses in applied mathematics, computer science, automatic control and electrical engineering.
Author: A. Ravishankar Rao Publisher: Springer Science & Business Media ISBN: 1461407249 Category : Medical Languages : en Pages : 234
Book Description
A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps [6], and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural network models. A promising path that is being explored is to study the importance of synchronization in biological systems. Synchronization plays a critical role in the interactions between neurons in the brain, giving rise to perceptual phenomena, and explaining multiple effects such as visual contour integration, and the separation of superposed inputs. The purpose of this book is to provide a unified view of how the time domain can be effectively employed in neural network models. A first direction to consider is to deploy oscillators that model temporal firing patterns of a neuron or a group of neurons. There is a growing body of research on the use of oscillatory neural networks, and their ability to synchronize under the right conditions. Such networks of synchronizing elements have been shown to be effective in image processing and segmentation tasks, and also in solving the binding problem, which is of great significance in the field of neuroscience. The oscillatory neural models can be employed at multiple scales of abstraction, ranging from individual neurons, to groups of neurons using Wilson-Cowan modeling techniques and eventually to the behavior of entire brain regions as revealed in oscillations observed in EEG recordings. A second interesting direction to consider is to understand the effect of different neural network topologies on their ability to create the desired synchronization. A third direction of interest is the extraction of temporal signaling patterns from brain imaging data such as EEG and fMRI. Hence this Special Session is of emerging interest in the brain sciences, as imaging techniques are able to resolve sufficient temporal detail to provide an insight into how the time domain is deployed in cognitive function. The following broad topics will be covered in the book: Synchronization, phase-locking behavior, image processing, image segmentation, temporal pattern analysis, EEG analysis, fMRI analyis, network topology and synchronizability, cortical interactions involving synchronization, and oscillatory neural networks. This book will benefit readers interested in the topics of computational neuroscience, applying neural network models to understand brain function, extracting temporal information from brain imaging data, and emerging techniques for image segmentation using oscillatory networks
Author: J. Mizera-Pietraszko Publisher: IOS Press ISBN: 161499773X Category : Computers Languages : en Pages : 284
Book Description
Web technology is ubiquitous in modern life, enabling various forms of communication in real time between the users and computers, as well as between network devices, by means of artificial (markup) languages and cascading style sheets (CSS). Multimedia packages implemented in the WWW can also further expand the user groups to include, for example, the amblyopic or the hearing-impaired. According to Microsoft, Web technology also encompasses Web servers and programming languages for building Web applications. But such a breathtaking development that meets dynamically changing new emerging networking standards demands a large-scale infrastructure that will enable us to access digital information in its every form, whatever its purpose. This book presents 20 papers and 3 keynote speeches from the 8th International Conference on Applications of Digital Information and Web Technologies (ICADIWT 2017), held at the Universidad Autόnoma de Ciudad Juárez, Juárez City, Chihuahua, Mexico, in March 2017. Over the years, the ICADIWT conference has created its own research community of participants from many countries, who attend the event each year to demonstrate and discuss their research findings. The community is growing every year. The scope of the ICADIWT 2017 conference covers a wide range of research areas, and the papers in the book are divided into 7 subject areas: pattern recognition; distributed computing; mobile technologies; digital technologies for aerospace; medical systems applications; system engineering; and control systems.
Author: Stephen Grossberg Publisher: Psychology Press ISBN: 1317769635 Category : Psychology Languages : en Pages : 395
Book Description
Mathematical Psychology and Psychophysiology promotes an understanding of the mind and its neural substrates by applying interdisciplinary approaches to issues concerning behavior and the brain. The contributions present model from many disciplines that share common, conceptual, functional, or mechanistic substrates and summarize recent models and data from neural networks, mathematical genetics, psychoacoustics, olfactory coding, visual perception, measurement, psychophysics, cognitive development, and other areas. The contributors to Mathematical Psychology and Psychophysiology show the conceptual and mathematical interconnectedness of several approaches to the fundamental scientific problem of understanding mind and brain. The book's interdisciplinary approach permits a deeper understanding of theoretical advances as it formally structures a broad overview of the data.
Author: John Choma
Author: John Choma
Author: Colin Fyfe
Author: John Choma
Author: Giuseppe Ciaburro
Author: Karl Johan Åström
Author: 
Author: Puyin Liu
Author: A. Ravishankar Rao
Author: J. Mizera-Pietraszko
Author: Stephen Grossberg