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Author: John Choma
Publisher: World Scientific
ISBN: 9810227701
Category : Technology & Engineering
Languages : en
Pages : 886
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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
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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
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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
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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: Irwin David Olin
Publisher:
ISBN:
Category :
Languages : en
Pages : 136
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Book Description
Author: Giuseppe Ciaburro
Publisher: Packt Publishing Ltd
ISBN: 1788399412
Category : Computers
Languages : en
Pages : 264
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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: Stanford University. Stanford Electronics Laboratories
Publisher:
ISBN:
Category : Feedback control systems
Languages : en
Pages : 82
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Book Description
The purpose of the research described herein was to develop convergent automatic-synthesis procedures for sampled-data networks with feedback, when the input and desired response sequences for the network are given and the network is of a fixed order. The capability of iterative synthesis using performance feedback has been obtained. The conventional sum-squared-error performance criterion was found to result in error surfaces with multiple minima when applied to the synthesis of linear sampled-data networks with feedback. A modified quadratic error criterion was developed, consisting of the sum of the squares of the difference between the desired output of the network and the actual network output during synthesis, with the modification that the desired output, rather than the network output, was fed back. Minimization of this criterion is shown to converge to a unique solution. For the order of network chosen, if a solution exists that can yield exactly the desired response for the given input sequence, it is shown that minimization of the modified performance criterion will achieve this solution. (Author).
Author: Wai-Kai Chen
Publisher: Hemisphere Pub
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 504
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Book Description
Author: Karl Johan Åström
Publisher: Princeton University Press
ISBN: 069121347X
Category : Technology & Engineering
Languages : en
Pages : 523
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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: Jeannette Lawrence
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 366
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Book Description
Author: Gordon E. Lynn
Publisher:
ISBN:
Category : Amplificadores magnéticos
Languages : en
Pages : 248
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Book Description
