Mathematical Treatment of Nanomaterials and Neural Networks
Author: Jia-Bao Liu
Publisher: Frontiers Media SA
Published: 2021-12-03
Total Pages: 239
ISBN-13: 2889717976
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Discrete Mathematics of Neural Networks
Author: Martin Anthony
Publisher: SIAM
Published: 2001-01-01
Total Pages: 137
ISBN-13: 089871480X
DOWNLOAD EBOOK →This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory. It considers select areas of discrete mathematics linking combinatorics and the theory of the simplest types of artificial neural networks. Neural networks have emerged as a key technology in many fields of application, and an understanding of the theories concerning what such systems can and cannot do is essential. Some classical results are presented with accessible proofs, together with some more recent perspectives, such as those obtained by considering decision lists. In addition, probabilistic models of neural network learning are discussed. Graph theory, some partially ordered set theory, computational complexity, and discrete probability are among the mathematical topics involved. Pointers to further reading and an extensive bibliography make this book a good starting point for research in discrete mathematics and neural networks.
Mathematics of Neural Networks
Author: Stephen W. Ellacott
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 423
ISBN-13: 1461560993
DOWNLOAD EBOOK →This volume of research papers comprises the proceedings of the first International Conference on Mathematics of Neural Networks and Applications (MANNA), which was held at Lady Margaret Hall, Oxford from July 3rd to 7th, 1995 and attended by 116 people. The meeting was strongly supported and, in addition to a stimulating academic programme, it featured a delightful venue, excellent food and accommo dation, a full social programme and fine weather - all of which made for a very enjoyable week. This was the first meeting with this title and it was run under the auspices of the Universities of Huddersfield and Brighton, with sponsorship from the US Air Force (European Office of Aerospace Research and Development) and the London Math ematical Society. This enabled a very interesting and wide-ranging conference pro gramme to be offered. We sincerely thank all these organisations, USAF-EOARD, LMS, and Universities of Huddersfield and Brighton for their invaluable support. The conference organisers were John Mason (Huddersfield) and Steve Ellacott (Brighton), supported by a programme committee consisting of Nigel Allinson (UMIST), Norman Biggs (London School of Economics), Chris Bishop (Aston), David Lowe (Aston), Patrick Parks (Oxford), John Taylor (King's College, Lon don) and Kevin Warwick (Reading). The local organiser from Huddersfield was Ros Hawkins, who took responsibility for much of the administration with great efficiency and energy. The Lady Margaret Hall organisation was led by their bursar, Jeanette Griffiths, who ensured that the week was very smoothly run.
Mathematical Approaches to Neural Networks
Author: J.G. Taylor
Publisher: Elsevier
Published: 1993-10-27
Total Pages: 391
ISBN-13: 0080887392
DOWNLOAD EBOOK →The subject of Neural Networks is being seen to be coming of age, after its initial inception 50 years ago in the seminal work of McCulloch and Pitts. It is proving to be valuable in a wide range of academic disciplines and in important applications in industrial and business tasks. The progress being made in each approach is considerable. Nevertheless, both stand in need of a theoretical framework of explanation to underpin their usage and to allow the progress being made to be put on a firmer footing. This book aims to strengthen the foundations in its presentation of mathematical approaches to neural networks. It is through these that a suitable explanatory framework is expected to be found. The approaches span a broad range, from single neuron details to numerical analysis, functional analysis and dynamical systems theory. Each of these avenues provides its own insights into the way neural networks can be understood, both for artificial ones and simplified simulations. As a whole, the publication underlines the importance of the ever-deepening mathematical understanding of neural networks.
Mathematical Aspects of Spin Glasses and Neural Networks
Author: Anton Bovier
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 381
ISBN-13: 1461241022
DOWNLOAD EBOOK →Applied Artificial Neural Network Methods For Engineers And Scientists: Solving Algebraic Equations
Author: Snehashish Chakraverty
Publisher: World Scientific
Published: 2021-01-26
Total Pages: 192
ISBN-13: 9811230226
DOWNLOAD EBOOK →The aim of this book is to handle different application problems of science and engineering using expert Artificial Neural Network (ANN). As such, the book starts with basics of ANN along with different mathematical preliminaries with respect to algebraic equations. Then it addresses ANN based methods for solving different algebraic equations viz. polynomial equations, diophantine equations, transcendental equations, system of linear and nonlinear equations, eigenvalue problems etc. which are the basic equations to handle the application problems mentioned in the content of the book. Although there exist various methods to handle these problems, but sometimes those may be problem dependent and may fail to give a converge solution with particular discretization. Accordingly, ANN based methods have been addressed here to solve these problems. Detail ANN architecture with step by step procedure and algorithm have been included. Different example problems are solved with respect to various application and mathematical problems. Convergence plots and/or convergence tables of the solutions are depicted to show the efficacy of these methods. It is worth mentioning that various application problems viz. Bakery problem, Power electronics applications, Pole placement, Electrical Network Analysis, Structural engineering problem etc. have been solved using the ANN based methods.
An Introduction to Neural Network Methods for Differential Equations
Author: Neha Yadav
Publisher: Springer
Published: 2015-02-26
Total Pages: 124
ISBN-13: 9401798168
DOWNLOAD EBOOK →This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed interest of the 1980s. A general introduction to neural networks and learning technologies is presented in Section III. This section also includes the description of the multilayer perceptron and its learning methods. In Section IV, the different neural network methods for solving differential equations are introduced, including discussion of the most recent developments in the field. Advanced students and researchers in mathematics, computer science and various disciplines in science and engineering will find this book a valuable reference source.
The Math of Neural Networks
Author: Michael Taylor
Publisher: Independently Published
Published: 2017-10-04
Total Pages: 168
ISBN-13: 9781549893643
DOWNLOAD EBOOK →There are many reasons why neural networks fascinate us and have captivated headlines in recent years. They make web searches better, organize photos, and are even used in speech translation. Heck, they can even generate encryption. At the same time, they are also mysterious and mind-bending: how exactly do they accomplish these things ? What goes on inside a neural network?On a high level, a network learns just like we do, through trial and error. This is true regardless if the network is supervised, unsupervised, or semi-supervised. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. In the following chapters we will unpack the mathematics that drive a neural network. To do this, we will use a feedforward network as our model and follow input as it moves through the network.
Handbook of Functionalized Nanomaterials
Author: Chaudhery Mustansar Hussain
Publisher: Elsevier
Published: 2021-08-23
Total Pages: 406
ISBN-13: 0128224355
DOWNLOAD EBOOK →Handbook of Functionalized Nanomaterials: Environmental Health and Safety discusses the reactive properties of FNMs used in a range of applications, and their toxic impact on the environment. Nanomaterials have unique properties that can make them highly reactive. This reactivity can cause unwanted interactions with living cells, an increase in oxidative stress or damage to genetic material - resulting in damage to the environment and local wildlife. This negative impact is often further increased after surface functionalization of nanomaterials with other materials which offer unique properties of their own. To ensure environmental safety and ecological balance, rigorous toxicity testing of functionalized nanomaterials (FNMs) is necessary. This book discusses the toxicological uncertainties of FNMs and the limitations of FNMs in a range of applications. Later chapters propose methods to reliably assess the harm that functionalized nanomaterials can cause to the environment and wildlife, as well covering recent developments in the field of environmental health safety. The book concludes with a discussion on the future prospects of safe functionalized nanomaterials. Offers a novel, integrated approach, bridging the gap between FNMs and environmental health and safety Analyses the reactive properties of FNMs and their toxicological potential Provides an in-depth look at the impact of functionalized nanomaterials on the environment
Zhang Neural Networks and Neural-dynamic Method
Author: Yunong Zhang
Publisher:
Published: 2011
Total Pages: 0
ISBN-13: 9781616688394
DOWNLOAD EBOOK →The real-time solution to a mathematical problem arises in numerous fields of science, engineering, and business. This book presents ZNN, ZD or ZND theory that formalises these problems and solutions in the time-varying context, and provides compact models that could solve those dynamic problems.
