Neural Networks For Electronics Hobbyists
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Neural Networks For Electronics Hobbyists
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Author : Richard McKeon
language : en
Publisher: Apress
Release Date : 2018-04-10
Neural Networks For Electronics Hobbyists written by Richard McKeon and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-10 with Computers categories.
Learn how to implement and build a neural network with this non-technical, project-based book as your guide. As you work through the chapters, you'll build an electronics project, providing a hands-on experience in training a network. There are no prerequisites here and you won't see a single line of computer code in this book. Instead, it takes a hardware approach using very simple electronic components. You'll start off with an interesting non-technical introduction to neural networks, and then construct an electronics project. The project isn't complicated, but it illustrates how back propagation can be used to adjust connection strengths or "weights" and train a network. By the end of this book, you'll be able to take what you've learned and apply it to your own projects. If you like to tinker around with components and build circuits on a breadboard, Neural Networks for Electronics Hobbyists is the book for you. What You'll Learn Gain a practical introduction to neural networks Review techniques for training networks with electrical hardware and supervised learning Understand how parallel processing differs from standard sequential programming Who This Book Is For Anyone interest in neural networks, from electronic hobbyists looking for an interesting project to build, to a layperson with no experience. Programmers familiar with neural networks but have only implemented them using computer code will also benefit from this book.
Neural Networks For Electronics Hobbyists
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Author : Rick McKeon
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-10-18
Neural Networks For Electronics Hobbyists written by Rick McKeon and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-18 with categories.
This book is for the layman and the electronics hobbyist who wants to know a little more about neural networks. We start off with an interesting nontechnical introduction to neural networks, and then we build a fun electronics project to give you some hands-on experience in training a network. There are no prerequisites here. You don't need an engineering degree and you don't even need to understand high school math in order to understand everything we are going to discuss. You won't see a single line of computer code in this book. If you like to tinker around with components and build circuits on a breadboard, you're going to love this project! Who knows, if you enjoy this brief introduction, you may want to pursue this fascinating topic further!
Vlsi Artificial Neural Networks Engineering
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Author : Mohamed I. Elmasry
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Vlsi Artificial Neural Networks Engineering written by Mohamed I. Elmasry and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Engineers have long been fascinated by how efficient and how fast biological neural networks are capable of performing such complex tasks as recognition. Such networks are capable of recognizing input data from any of the five senses with the necessary accuracy and speed to allow living creatures to survive. Machines which perform such complex tasks as recognition, with similar ac curacy and speed, were difficult to implement until the technological advances of VLSI circuits and systems in the late 1980's. Since then, the field of VLSI Artificial Neural Networks (ANNs) have witnessed an exponential growth and a new engineering discipline was born. Today, many engineering curriculums have included a course or more on the subject at the graduate or senior under graduate levels. Since the pioneering book by Carver Mead; "Analog VLSI and Neural Sys tems", Addison-Wesley, 1989; there were a number of excellent text and ref erence books on the subject, each dealing with one or two topics. This book attempts to present an integrated approach of a single research team to VLSI ANNs Engineering.
Complex Valued Neural Networks
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Author : Akira Hirose
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-08
Complex Valued Neural Networks written by Akira Hirose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-08 with Computers categories.
Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and superconducting waves. This fact is a critical advantage in practical applications in diverse fields of engineering, where signals are routinely analyzed and processed in time/space, frequency, and phase domains. Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely subject. Demonstrating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of: Conventional complex-valued neural networks Quaternionic neural networks Clifford-algebraic neural networks Presented by international experts in the field, Complex-Valued Neural Networks: Advances and Applications is ideal for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians interested in computational intelligence, artificial intelligence, machine learning theories, and algorithms.
Stability Analysis Of Neural Networks
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Author : Grienggrai Rajchakit
language : en
Publisher: Springer Nature
Release Date : 2021-12-05
Stability Analysis Of Neural Networks written by Grienggrai Rajchakit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-05 with Mathematics categories.
This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists.
A Practical Guide To Neural Nets
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Author : Marilyn McCord Nelson
language : en
Publisher: Addison Wesley Publishing Company
Release Date : 1994
A Practical Guide To Neural Nets written by Marilyn McCord Nelson and has been published by Addison Wesley Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.
Based on a course given to internal managers at Texas Instruments, this book is an introduction to neural nets for computer science, artificial intelligence and R & D professionals, as well as MIS or DP managers.
Neural Networks
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Author : S?ren Brunak
language : en
Publisher: World Scientific
Release Date : 1990
Neural Networks written by S?ren Brunak and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.
Artificial Neural Networks
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Author : Nelson Morgan
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 1990
Artificial Neural Networks written by Nelson Morgan and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Artificial intelligence categories.
Neural Networks For Identification Prediction And Control
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Author : Duc T. Pham
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Networks For Identification Prediction And Control written by Duc T. Pham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described.
Embedded Deep Learning
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Author : Bert Moons
language : en
Publisher: Springer
Release Date : 2018-10-23
Embedded Deep Learning written by Bert Moons and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-23 with Technology & Engineering categories.
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.