Fundamentals of Artificial Neural Networks

Important results are integrated into the text in order to explain a wide range of existing empirical observations and commonly used heuristics.

Fundamentals of Artificial Neural Networks

Author: Mohamad H. Hassoun

Publisher: MIT Press

ISBN: 9780262082396

Page: 511

View: 267

A systematic account of artificial neural network paradigms that identifies fundamental concepts and major methodologies. Important results are integrated into the text in order to explain a wide range of existing empirical observations and commonly used heuristics.

Artificial Neural Networks

This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and ...

Artificial Neural Networks

Author: Kevin L. Priddy

Publisher: SPIE Press

ISBN: 9780819459879

Page: 165

View: 855

This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.

Neural Networks in Robotics

In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book.

Neural Networks in Robotics

Author: George A. Bekey

Publisher: Springer Science & Business Media

ISBN: 1461531802

Page: 563

View: 131

Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book. On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented. For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.

Artificial Neural Nets and Genetic Algorithms

This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning.

Artificial Neural Nets and Genetic Algorithms

Author: Rudolf F. Albrecht

Publisher: Springer Science & Business Media

ISBN: 370917533X

Page: 737

View: 558

Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.

Principles of Artificial Neural Networks

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.

Principles of Artificial Neural Networks

Author: Daniel Graupe

Publisher: World Scientific

ISBN: 9814522740

Page: 364

View: 635

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 OCo 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. 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."

An Introduction to Neural Networks

As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, ...

An Introduction to Neural Networks

Author: Kevin Gurney

Publisher: CRC Press

ISBN: 1482286998

Page: 234

View: 395

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

Strengthening Deep Neural Networks

Now let's consider the additional complications brought about by the environment
and relative positioning of the speaker/audio: Environment A fundamental
challenge to launching a physical adversarial audio attack is that adversarial
sounds ...

Strengthening Deep Neural Networks

Author: Katy Warr

Publisher: O'Reilly Media

ISBN: 149204492X

Page: 246

View: 599

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come

Neural Networks

In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets.

Neural Networks

Author: Raul Rojas

Publisher: Springer Science & Business Media

ISBN: 9783540605058

Page: 502

View: 815

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

Elements of Artificial Neural Networks

Software implementing many commonly used neural network algorithms is available at the book's website. Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text.

Elements of Artificial Neural Networks

Author: Kishan Mehrotra

Publisher: MIT Press

ISBN: 9780262133289

Page: 344

View: 550

Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods. The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout. Software implementing many commonly used neural network algorithms is available at the book's website. Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text.

Neural Networks In Biomedicine Proceedings Of The Advanced School Of The Italian Bromedical Physics Association

But organisms can also determine or influence their objective environment by
displacing, modify, creating, destroying, etc. parts of the objective environment.
Neural networks that live in environment live in an objective environment,
although ...

Neural Networks In Biomedicine   Proceedings Of The Advanced School Of The Italian Bromedical Physics Association

Author: Masulli Francesco

Publisher: World Scientific

ISBN: 9814551538

Page: 416

View: 810

This book contains a collection of essays written in honor of Wolfhart Zimmermann's 80th birthday, most of them based on talks presented at a symposium in his honor.The book shows the unifying force of a subject (Quantum Field Theory) and a person (Zimmermann). It ranges from fundamental questions in quantum physics over applications to particle physics and noncommutative geometry to the latest developments in many body theory and dynamical systems. These key ideas are elucidated by worldwide-recognized experts including Faddeev, Becchi, Buchholz, Lowenstein and Salmhofer.Readers seeking examples on how a subject has evolved, diversified and deepened over the course of several decades and how a single person can influence this process can find here a perfect illustration. Altogether, readers are treated to a high-brow intellectual adventure.

Applications of Neural Networks

The book is divided into three sections. Section A is an introduction to neural networks for nonspecialists. Section B looks at examples of applications using `Supervised Training'.

Applications of Neural Networks

Author: Alan Murray

Publisher: Springer Science & Business Media

ISBN: 1475723792

Page: 322

View: 534

Applications of Neural Networks gives a detailed description of 13 practical applications of neural networks, selected because the tasks performed by the neural networks are real and significant. The contributions are from leading researchers in neural networks and, as a whole, provide a balanced coverage across a range of application areas and algorithms. The book is divided into three sections. Section A is an introduction to neural networks for nonspecialists. Section B looks at examples of applications using `Supervised Training'. Section C presents a number of examples of `Unsupervised Training'. For neural network enthusiasts and interested, open-minded sceptics. The book leads the latter through the fundamentals into a convincing and varied series of neural success stories -- described carefully and honestly without over-claiming. Applications of Neural Networks is essential reading for all researchers and designers who are tasked with using neural networks in real life applications.

Artificial Neural Networks in Biomedicine

This volume provides a state-of-the-art survey of artificial neural network applications in biomedical diagnosis, laboratory data analysis and related practical areas.

Artificial Neural Networks in Biomedicine

Author: Paulo J.G. Lisboa

Publisher: Springer Science & Business Media

ISBN: 9781852330057

Page: 287

View: 848

This volume provides a state-of-the-art survey of artificial neural network applications in biomedical diagnosis, laboratory data analysis and related practical areas. It looks at biomedical applications which involve customising neural network technology to resolve specific difficulties with data processing, and deals with applications relating to particular aspects of clinical practice and laboratory or medically-related analysis. Each chapter is self-contained with regard to the technology used, covering important technical points and implementation issues like the design of user interfaces and hardware/software platforms. Artificial Neural Networks in Biomedicine will be of interest to computer scientists and neural network practitioners who want to extend their knowledge of issues relevant to biomedical applications, developers of clinical computer systems, and medical researchers looking for new methods and computational tools.

Artificial Neural Networks for Engineering Applications

... of three differentshaped bio-digesters for biogas production and optimization
by artificial neural network integrated with ... production in wastewater treatment
plants: state of the art, Science of the Total Environment 587–588 (2017) 510–
521.

Artificial Neural Networks for Engineering Applications

Author: Alma Y. Alanis

Publisher: Academic Press

ISBN: 0128182474

Page: 224

View: 345

Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the book contains all the theory required to use the proposed methodologies for different applications. Presents the current trends for the solution of complex engineering problems that cannot be solved through conventional methods Includes real-life scenarios where a wide range of artificial neural network architectures can be used to solve the problems encountered in engineering Contains all the theory required to use the proposed methodologies for different applications

Artificial Neural Networks

The purpose of this book is to provide recent advances of artificial neural networks in biomedical applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization.

Artificial Neural Networks

Author: Kenji Suzuki

Publisher: BoD – Books on Demand

ISBN: 9533072431

Page: 376

View: 806

Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. The purpose of this book is to provide recent advances of artificial neural networks in biomedical applications. The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. Advanced architectures for biomedical applications, which offer improved performance and desirable properties, follow. Parts continue with biological applications such as gene, plant biology, and stem cell, medical applications such as skin diseases, sclerosis, anesthesia, and physiotherapy, and clinical and other applications such as clinical outcome, telecare, and pre-med student failure prediction. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in biomedical areas. The target audience includes professors and students in engineering and medical schools, researchers and engineers in biomedical industries, medical doctors, and healthcare professionals.

Neural Network Applications in Control

Introducing a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks as well as the more useful multilayer perception back-propagation networks, this book aims to give a detailed appreciation ...

Neural Network Applications in Control

Author: Institution of Electrical Engineers

Publisher: IET

ISBN: 9780852968529

Page: 295

View: 648

Introducing a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks as well as the more useful multilayer perception back-propagation networks, this book aims to give a detailed appreciation of the use of neural nets in these applications.

Artificial Neural Networks as Models of Neural Information Processing

The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing.

Artificial Neural Networks as Models of Neural Information Processing

Author: Marcel van Gerven

Publisher: Frontiers Media SA

ISBN: 2889454010

Page:

View: 164

Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.