Bøger i Advanced Series In Circuits And Systems serien

Suitable as a self-study course for engineers and computer scientists in the industry, this book covers major neural network approaches and architectures with the theories. It presents detailed case studies for each of the approaches, accompanied with computer codes and the corresponding computed results.

Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained. The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks.

Network scattering parameters are powerful tools for the analysis and design of HF and microwave networks. The aim of this book is to give a thorough working knowledge of scattering parameters and their applications in circuit analysis and design.

The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks - demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

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.