Author: Martin T. HaganPublisher:
ISBN: 9789812403766
Category : Neural networks (Computer science)
Languages : en
Pages :
Book Description
Neural Network Design
Author: Martin T. HaganPublisher:
ISBN: 9789812403766
Category : Neural networks (Computer science)
Languages : en
Pages :
Book Description
Neural Network Design and the Complexity of Learning
Author: J. Stephen JuddPublisher: MIT Press
ISBN: 9780262100458
Category : Computers
Languages : en
Pages : 188
Book Description
Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier.Judd looks beyond the scope of any one particular learning rule, at a level above the details of neurons. There he finds new issues that arise when great numbers of neurons are employed and he offers fresh insights into design principles that could guide the construction of artificial and biological neural networks.The first part of the book describes the motivations and goals of the study and relates them to current scientific theory. It provides an overview of the major ideas, formulates the general learning problem with an eye to the computational complexity of the task, reviews current theory on learning, relates the book's model of learning to other models outside the connectionist paradigm, and sets out to examine scale-up issues in connectionist learning.Later chapters prove the intractability of the general case of memorizing in networks, elaborate on implications of this intractability and point out several corollaries applying to various special subcases. Judd refines the distinctive characteristics of the difficulties with families of shallow networks, addresses concerns about the ability of neural networks to generalize, and summarizes the results, implications, and possible extensions of the work. Neural Network Design and the Complexity of Learning is included in the Network Modeling and Connectionism series edited by Jeffrey Elman.
Deep Neural Network Design for Radar Applications
Author: Sevgi Zubeyde GurbuzPublisher: SciTech Publishing
ISBN: 1785618520
Category : Technology & Engineering
Languages : en
Pages : 419
Book Description
Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of.
Deep Learning Neural Networks: Design And Case Studies
Author: Daniel GraupePublisher: World Scientific Publishing Company
ISBN: 9813146478
Category : Computers
Languages : en
Pages : 280
Book Description
Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance.This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research.
Recurrent Neural Networks
Author: Larry MedskerPublisher: CRC Press
ISBN: 9781420049176
Category : Computers
Languages : en
Pages : 414
Book Description
With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks. This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects. The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.
Mathematical Methods for Neural Network Analysis and Design
Author: Richard M. GoldenPublisher: MIT Press
ISBN: 9780262071741
Category : Computers
Languages : en
Pages : 452
Book Description
For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion.
Neural Networks In Design And Manufacturing
Author: Yoshiyasu TakefujiPublisher: World Scientific
ISBN: 9814504564
Category : Computers
Languages : en
Pages : 319
Book Description
Over the past few years, there has been a surge of research activities on artificial neural networks. Although the thrust originally came from computer scientists and electrical engineers, neural network research has recently attracted researchers in the fields of operations research, operations management and industrial engineering.Despite the huge volume of recent publications devoted to neural network research, there is no single monograph addressing the potential roles of artificial neural networks for design and manufacturing.The focus of this book is on the applications of neural network concepts and techniques to design and manufacturing. This book reviews the state-of-the-art of the research activities, highlights the recent advances in research and development, and discusses the potential directions and future trends along this stream of research.The potential readers of this book will include, but are not limited to, beginners, professionals and practitioners in industries who are applying neural networks to design and manufacturing.The topics include conceptual design, group technology, process planning and scheduling, process monitoring and others.
Neural Networks and Systolic Array Design
Author: Sankar K. PalPublisher: World Scientific
ISBN: 981277808X
Category : Computers
Languages : en
Pages : 421
Book Description
Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.
Neural Networks for RF and Microwave Design
Author: Q. J. ZhangPublisher: Artech House Publishers
ISBN:
Category : Computers
Languages : en
Pages : 396
Book Description
Discover the new, unconventional alternatives for conquering RF and microwave design and modeling problems using neural networks -- information processing systems that can learn, generalize, and even allow model development when component formulas are missing -- with this book and software package. It shows you the ease of creating models with neural networks, and how quick model evaluation can be done, plus other opportunities presented by neural networks for conquering the toughest RF and microwave CAD problems.
Neural Networks and Deep Learning
Author: Charu C. AggarwalPublisher: Springer
ISBN: 3319944630
Category : Computers
Languages : en
Pages : 512
Book Description
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.