Articles by "Neural Networks"

Showing posts with label Neural Networks. Show all posts

Joseph P. Bigus ... 220 pages - Publisher: Mcgraw-Hill; (May, 1996) ... Language: English - ISBN-10: 0070057796 - ISBN-13: 978-0070057791 ...

Readers will find concrete implementation strategies, reinforced with real-world business examples and a minimum of formulas, and case studies drawn from a broad range of industries. The book illustrates the popular data mining functions of classification, clustering, modeling, and time-series forecasting--through examples developed using the IBM Neural Network Utility.

Simon Haykin ... 842 pages - Publisher: Prentice Hall; 2nd edition (July, 1998) ... Language: English - ISBN-10: 0132733501 - ISBN-13: 978-0132733502 ...

This text represents the first comprehensive treatment of neural networks from an engineering perspective. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology. Neural Networks provides broad coverage of the subject, including the learning process, back propogation, radial basis functions, recurrent networks, self-organizing systems, modular networks, temporal processing, neurodynamics, and VLSI implementations. Chapter objectives, computer experiments, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary reinforce key concepts. The author's concise and fluid writing style makes the material more accessible. For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

Nazmul Siddique, Hojjat Adeli ... 532 pages - Publisher: Wiley; 1st edition (May, 2013) ... Language: English - ISBN-10: 1118337840 - ISBN-13: 978-1118337844 ...

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapter - Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems - Considers real world problems in the domain of systems modelling, control and optimization - Contains a foreword written by Lotfi Zadeh. Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

Shuming Wang, Junzo Watada ... 248 pages - Publisher: Springer; (March, 2012) ... Language: English - ISBN-10: 1441995595 - ISBN-13: 978-1441995599 ...

Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies. The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins by outlining the history and development of the fuzzy random variable before detailing numerous optimization models and applications that include the design of system controls for a dam.

Phil Kim ... 151 pages - Publisher: Apress; 1st edition (June, 2017) ... Language: English - ISBN-10: 1484228448 - ISBN-13: 978-1484228449 ...

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn: Use MATLAB for deep learning * Discover neural networks and multi-layer neural networks * Work with convolution and pooling layers * Build a MNIST example with these layers. Who This Book Is For: Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

Hugh Cartwright ... 340 pages - Publisher: Springer; 2nd edition (December, 2014) ... Language: English - ISBN-10: 1493922386 - ISBN-13: 978-1493922383 ...

This volume presents examples of how ANNs are applied in biological sciences and related areas. Chapters focus on the analysis of intracellular sorting information, prediction of the behavior of bacterial communities, biometric authentication, studies of Tuberculosis, gene signatures in breast cancer classification, use of mass spectrometry in metabolite identification, visual navigation, and computer diagnosis. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Artificial Neural Networks: Second Edition aids scientists in continuing to study Artificial Neural Networks (ANNs).

Oliver Nelles ... 785 pages - Publisher: Springer; (December, 2000) ... Language: English - ISBN-10: 3540673695 - ISBN-13: 978-3540673699 ...

Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

Marvin L. ... 418 pages - Publisher: CreateSpace Independent Publishing Platform; (October, 2016) ... Language: English - ISBN-10: 1539701956 - ISBN-13: 978-1539701958 ...

Neural Network Toolbox provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more importan features are de next: •Deep learning, including convolutional neural networks and autoencoders •Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox •Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) •Unsupervised learning algorithms, including self-organizing maps and competitive layers •Apps for data-fitting, pattern recognition, and clustering •Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance •Simulink blocks for building and evaluating neural networks and for control systems applications.

Kevin L. Priddy, Paul E. Keller ... 180 pages - Publisher: SPIE Publications; (August, 2005) ... Language: English - ISBN-10: 0819459879 - ISBN-13: 978-0819459879 ...

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. Contents: - Preface - Acknowledgments - Introduction - Learning Methods - Data Normalization - Data Collection, Preparation, Labeling, and Input Coding - Output Coding - Post-Processing - Supervised Training Methods - Unsupervised Training Methods - Recurrent Neural Networks - A Plethora of Applications - Dealing with Limited Amounts of Data - Appendix A: The Feedforward Neural Network - Appendix B: Feature Saliency - Appendix C: Matlab Code for Various Neural Networks - Appendix D: Glossary of Terms - References - Index.

Mathukumalli Vidyasagar ... 488 pages - Publisher: Springer; 2nd edition (November, 2002) ...
Language: English - ISBN-10: 1852333731 - ISBN-13: 978-1852333737 ...

Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as: How does a machine learn a new concept on the basis of examples? • How can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input? • How much training is required to achieve a specified level of accuracy in the prediction? • How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time?

In its successful first edition, A Theory of Learning and Generalization was the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side-by-side leads to new insights, as well as to new results in both topics. This second edition extends and improves upon this material, covering new areas including: • Support vector machines. • Fat-shattering dimensions and applications to neural network learning. • Learning with dependent samples generated by a beta-mixing process. • Connections between system identification and learning theory. • Probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithm. Reflecting advancements in the field, solutions to some of the open problems posed in the first edition are presented, while new open problems have been added. Learning and Generalization (second edition) is essential reading for control and system theorists, neural network researchers, theoretical computer scientists and probabilist.

Linda M. See, Robert Abrahart ... 316 pages- Publication Date: April, 2007 ...
Language: English - ASIN: B000PWQTEO by Amazon Digital Services ...

Neural Networks for Hydrological Modeling is intended to ease the transition into a data-driven paradigm and to provide a stimulus for further research on neural network hydrological modeling. Each chapter is the work of one or more independent experts in a different field of hydrological modeling - which demonstrates the wide potential of such tools, for solving real world problems throughout all areas of hydrological interest, and at all levels of methodological investigation. The initial material provides a basic introduction to the concepts and technologies involved; subsequent chapters use hydrological examples and case studies to illustrate the practical application of different mechanisms and approaches. The final chapter suggests some useful research directions, based on lateral thinking and alternative strategies or mindsets.

Jeff Heaton ... 119 pages - Publisher: Heaton Research, Inc.; (April 3, 2012) ...
Language: English - ASIN: B00845UQL6 ...

This book introduces the reader to the basic math used for neural network calculation. This book assumes the reader has only knowledge of college algebra and computer programming. This book begins by showing how to calculate output of a neural network and moves on to more advanced training methods such as backpropagation, resilient propagation and Levenberg Marquardt optimization. The mathematics needed by these techniques is also introduced. Mathematical topics covered by this book include first, second, Hessian matrices, gradient descent and partial derivatives. All mathematical notation introduced is explained. Neural networks covered include the feedforward neural network and the self organizing map. This book provides an ideal supplement to our other neural books. This book is ideal for the reader, without a formal mathematical background, that seeks a more mathematical description of neural networks.

Simon Haykin ... 936 pages - Publisher: PHIL; 3rd edition (2010) ... Language: English - ISBN-10: 8131763773 - ISBN-13: 978-8131763773 ...

The third edition of this classic book presents a comprehensive treatment of neural networks and learning machines. The book has been revised extensively to provide an up-to-date treatment of the subject.

Sandhya Samarasinghe ... 570 pages - Publisher: Auerbach Publications; (September, 2006) ... Language: English - ISBN-10: 084933375X - ISBN-13: 978-0849333750.

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features: Explains neural networks in a multi-disciplinary context § Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding § Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting § Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters. Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA. Her neural networks research focuses on theoretical understanding and advancements as well as practical implementations.

John A. Flores ... 410 pages - Publisher: Nova Science Publishers; (September 30, 2011) ...
Language: English - ISBN-10: 1613242859 - ISBN-13: 978-1613242858 ...

This book gathers the most current research from across the globe in the study of artificial neural networks. Topics discussed include artificial neural networks in environmental sciences and chemical engineering; application of artificial neural networks in the development of pharamceutical microemulsions; massive-training artificial neural networks for supervised enhancement/suppression of lesions/patterns in medical images; evidences of new biophysical properties of microtubules; neural network applications in modern induction machine control systems and wavelet neural networks.

Robert A. Dunne ... 288 pages - Publisher: Wiley-Interscience; 1st edition (July 16, 2007) ...
Language: English - ISBN-10: 0471741086 - ISBN-13: 978-0471741084 ...

An accessible and up-to-date treatment featuring the connection between neural networks and statistics
A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models.

This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers? - Could the model be made more robust? - Which points will have a high leverage? - What are good starting values for the fitting algorithm?

Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature.

Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS® codes that are available on the book's related Web site. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering.

Andrzej Janczak ... 199 pages - Publisher: Springer; (February, 2009) ... 
Language: English - ISBN-10: 3540231854 - ISBN-13: 978-3540231851 ...

This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models" is useful for researchers, engineers and graduate students in nonlinear systems and neural network theory.

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