Guide To Neural Computing Applications

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Guide to Neural Computing Applications

Lionel Tarassenko
Guide to Neural Computing Applications Book PDF

Author : Lionel Tarassenko
Publisher : Elsevier
Page : 160 pages
File Size : 47,5 Mb
Release : 1998-01-30
Category : Computers
ISBN : 9780080512600

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Guide to Neural Computing Applications by Lionel Tarassenko Pdf

Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities. A few simple algorithms will learn relationships between cause and effect or organise large volumes of data into orderly and informative patterns but they cannot solve every problem and consequently their application must be chosen carefully and appropriately. This book outlines how best to make use of neural networks. It enables newcomers to the technology to construct robust and meaningful non-linear models and classifiers and benefits the more experienced practitioner who, through over familiarity, might otherwise be inclined to jump to unwarranted conclusions. The book is an invaluable resource not only for those in industry who are interested in neural computing solutions, but also for final year undergraduates or graduate students who are working on neural computing projects. It provides advice which will help make the best use of the growing number of commercial and public domain neural network software products, freeing the specialist from dependence upon external consultants.

Handbook of Neural Computing Applications

Alianna J. Maren,Craig T. Harston,Robert M. Pap
Handbook of Neural Computing Applications Book PDF

Author : Alianna J. Maren,Craig T. Harston,Robert M. Pap
Publisher : Academic Press
Page : 472 pages
File Size : 41,5 Mb
Release : 2014-05-10
Category : Computers
ISBN : 9781483264844

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Handbook of Neural Computing Applications by Alianna J. Maren,Craig T. Harston,Robert M. Pap Pdf

Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.

Applying Neural Networks

Kevin Swingler
Applying Neural Networks Book PDF

Author : Kevin Swingler
Publisher : Morgan Kaufmann
Page : 348 pages
File Size : 43,6 Mb
Release : 1996
Category : Computers
ISBN : 0126791708

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Applying Neural Networks by Kevin Swingler Pdf

This book is designed to enable the reader to design and run a neural network-based project. It presents everything the reader will need to know to ensure the success of such a project. The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.

Guide to Convolutional Neural Networks

Hamed Habibi Aghdam,Elnaz Jahani Heravi
Guide to Convolutional Neural Networks Book PDF

Author : Hamed Habibi Aghdam,Elnaz Jahani Heravi
Publisher : Springer
Page : 282 pages
File Size : 46,7 Mb
Release : 2017-05-17
Category : Computers
ISBN : 9783319575506

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Guide to Convolutional Neural Networks by Hamed Habibi Aghdam,Elnaz Jahani Heravi Pdf

This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.

Geophysical Applications of Artificial Neural Networks and Fuzzy Logic

W. Sandham,M. Leggett
Geophysical Applications of Artificial Neural Networks and Fuzzy Logic Book PDF

Author : W. Sandham,M. Leggett
Publisher : Springer Science & Business Media
Page : 336 pages
File Size : 53,7 Mb
Release : 2013-06-29
Category : Mathematics
ISBN : 9789401702713

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Geophysical Applications of Artificial Neural Networks and Fuzzy Logic by W. Sandham,M. Leggett Pdf

The past fifteen years has witnessed an explosive growth in the fundamental research and applications of artificial neural networks (ANNs) and fuzzy logic (FL). The main impetus behind this growth has been the ability of such methods to offer solutions not amenable to conventional techniques, particularly in application domains involving pattern recognition, prediction and control. Although the origins of ANNs and FL may be traced back to the 1940s and 1960s, respectively, the most rapid progress has only been achieved in the last fifteen years. This has been due to significant theoretical advances in our understanding of ANNs and FL, complemented by major technological developments in high-speed computing. In geophysics, ANNs and FL have enjoyed significant success and are now employed routinely in the following areas (amongst others): 1. Exploration Seismology. (a) Seismic data processing (trace editing; first break picking; deconvolution and multiple suppression; wavelet estimation; velocity analysis; noise identification/reduction; statics analysis; dataset matching/prediction, attenuation), (b) AVO analysis, (c) Chimneys, (d) Compression I dimensionality reduction, (e) Shear-wave analysis, (f) Interpretation (event tracking; lithology prediction and well-log analysis; prospect appraisal; hydrocarbon prediction; inversion; reservoir characterisation; quality assessment; tomography). 2. Earthquake Seismology and Subterranean Nuclear Explosions. 3. Mineral Exploration. 4. Electromagnetic I Potential Field Exploration. (a) Electromagnetic methods, (b) Potential field methods, (c) Ground penetrating radar, (d) Remote sensing, (e) inversion.

Business Applications of Neural Networks

Bill Edisbury
Business Applications of Neural Networks Book PDF

Author : Bill Edisbury
Publisher : World Scientific
Page : 222 pages
File Size : 40,7 Mb
Release : 2000
Category : Business & Economics
ISBN : 9789812813312

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Business Applications of Neural Networks by Bill Edisbury Pdf

Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualise complex databases for marketing segmentation. This boom in applications covers a wide range of business interests - from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct quantitative studies to be carried out without the need for rocket-science expertise. This is a review of the state-of-the-art in applications of neural-network methods in three important areas of business analysis. It includes a tutorial chapter to introduce new users to the potential and pitfalls of this new technology.

Neural Networks and Deep Learning

Charu C. Aggarwal
Neural Networks and Deep Learning Book PDF

Author : Charu C. Aggarwal
Publisher : Springer
Page : 497 pages
File Size : 49,7 Mb
Release : 2018-08-25
Category : Computers
ISBN : 9783319944630

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Neural Networks and Deep Learning by Charu C. Aggarwal Pdf

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.

A Practical Guide to Neural Nets

Marilyn McCord Nelson,W. T. Illingworth
A Practical Guide to Neural Nets Book PDF

Author : Marilyn McCord Nelson,W. T. Illingworth
Publisher : Addison Wesley Publishing Company
Page : 360 pages
File Size : 43,6 Mb
Release : 1994
Category : Computers
ISBN : PSU:000025119084

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A Practical Guide to Neural Nets by Marilyn McCord Nelson,W. T. Illingworth Pdf

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.

Convolutional Neural Networks in Visual Computing

Ragav Venkatesan,Baoxin Li
Convolutional Neural Networks in Visual Computing Book PDF

Author : Ragav Venkatesan,Baoxin Li
Publisher : CRC Press
Page : 204 pages
File Size : 44,5 Mb
Release : 2017-10-23
Category : Computers
ISBN : 9781351650328

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Convolutional Neural Networks in Visual Computing by Ragav Venkatesan,Baoxin Li Pdf

This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.

Neural Networks

Michael Chester
Neural Networks Book PDF

Author : Michael Chester
Publisher : Prentice Hall
Page : 196 pages
File Size : 41,9 Mb
Release : 1993
Category : Computers
ISBN : MINN:31951D01024317M

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Neural Networks by Michael Chester Pdf

"Summarizing the status of the neural network field today, this comprehensive volume presents the software-based paradigms and the hardware implementations of neural networks and how they function. Michael Chester describes the mathematical foundations of the various neural network models, as well as those of fuzzy theory." "Sample applications are described in such areas as optical recognition of handwritten characters, speech recognition, machine control, and simulated biological evolution. Diagrams show neurons, their interconnections, and their behavior, as input patterns are presented to the networks. The step-by-step operations of networks are shown, both in their learning modes and their recall modes, as they categorize new input patterns on the basis of what was learned in the earlier phase." "Neural Networks: A Tutorial provides the ideal bridge from theoretical background to practical understanding of neural networks."--BOOK JACKET.Title Summary field provided by Blackwell North America, Inc. All Rights Reserved

Artificial Neural Networks

David J. Livingstone
Artificial Neural Networks Book PDF

Author : David J. Livingstone
Publisher : Humana Press
Page : 0 pages
File Size : 48,7 Mb
Release : 2011-10-09
Category : Computers
ISBN : 1617377384

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Artificial Neural Networks by David J. Livingstone Pdf

In this book, international experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. Methods involving the mapping and interpretation of Infra Red spectra and modelling environmental toxicology are included. This book is an excellent guide to this exciting field.

Artificial Neural Networks

Ivan Nunes da Silva,Danilo Hernane Spatti,Rogerio Andrade Flauzino,Luisa Helena Bartocci Liboni,Silas Franco dos Reis Alves
Artificial Neural Networks Book PDF

Author : Ivan Nunes da Silva,Danilo Hernane Spatti,Rogerio Andrade Flauzino,Luisa Helena Bartocci Liboni,Silas Franco dos Reis Alves
Publisher : Springer
Page : 307 pages
File Size : 49,8 Mb
Release : 2016-08-24
Category : Technology & Engineering
ISBN : 9783319431628

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Artificial Neural Networks by Ivan Nunes da Silva,Danilo Hernane Spatti,Rogerio Andrade Flauzino,Luisa Helena Bartocci Liboni,Silas Franco dos Reis Alves Pdf

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

Building Neural Networks

David M. Skapura
Building Neural Networks Book PDF

Author : David M. Skapura
Publisher : Addison-Wesley Professional
Page : 308 pages
File Size : 41,6 Mb
Release : 1996
Category : Computers
ISBN : 0201539217

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Building Neural Networks by David M. Skapura Pdf

Organized by application areas, rather than by specific network architectures or learning algorithms, Building Neural Networks shows why certain networks are more suitable than others for solving specific kinds of problems. Skapura also reviews principles of neural information processing and furnishes an operations summary of the most popular neural-network processing models.

Neural Networks

Berndt Müller,Joachim Reinhardt,Michael T. Strickland
Neural Networks Book PDF

Author : Berndt Müller,Joachim Reinhardt,Michael T. Strickland
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 45,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642577604

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Neural Networks by Berndt Müller,Joachim Reinhardt,Michael T. Strickland Pdf

Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.

Artificial Neural Networks in Medicine and Biology

H. Malmgren,M. Borga,L. Niklasson
Artificial Neural Networks in Medicine and Biology Book PDF

Author : H. Malmgren,M. Borga,L. Niklasson
Publisher : Springer Science & Business Media
Page : 339 pages
File Size : 46,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781447105138

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Artificial Neural Networks in Medicine and Biology by H. Malmgren,M. Borga,L. Niklasson Pdf

This book contains the proceedings of the conference ANNIMAB-l, held 13-16 May 2000 in Goteborg, Sweden. The conference was organized by the Society for Artificial Neural Networks in Medicine and Biology (ANNIMAB-S), which was established to promote research within a new and genuinely cross-disciplinary field. Forty-two contributions were accepted for presentation; in addition to these, S invited papers are also included. Research within medicine and biology has often been characterised by application of statistical methods for evaluating domain specific data. The growing interest in Artificial Neural Networks has not only introduced new methods for data analysis, but also opened up for development of new models of biological and ecological systems. The ANNIMAB-l conference is focusing on some of the many uses of artificial neural networks with relevance for medicine and biology, specifically: • Medical applications of artificial neural networks: for better diagnoses and outcome predictions from clinical and laboratory data, in the processing of ECG and EEG signals, in medical image analysis, etc. More than half of the contributions address such clinically oriented issues. • Uses of ANNs in biology outside clinical medicine: for example, in models of ecology and evolution, for data analysis in molecular biology, and (of course) in models of animal and human nervous systems and their capabilities. • Theoretical aspects: recent developments in learning algorithms, ANNs in relation to expert systems and to traditional statistical procedures, hybrid systems and integrative approaches.