Elements Of Artificial Neural Networks

Author : Kishan Mehrotra
Publisher : MIT Press
ISBN 13 : 9780262133289
Total Pages : 376 pages
Book Rating : 4.88/5 ( download)

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Download or read book Elements of Artificial Neural Networks written by Kishan Mehrotra and published by MIT Press. This book was released on 1997 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Sanjeev S. Tambe
Publisher : Simulation & Advanced Controls Incorporated
ISBN 13 : 9780965163903
Total Pages : 450 pages
Book Rating : 4.03/5 ( download)

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Download or read book Elements of Artificial Neural Networks with Selected Applications in Chemical Engineering, and Chemical and Biological Sciences written by Sanjeev S. Tambe and published by Simulation & Advanced Controls Incorporated. This book was released on 1996 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Osval Antonio Montesinos López
Publisher : Springer Nature
ISBN 13 : 3030890104
Total Pages : 707 pages
Book Rating : 4.00/5 ( download)

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Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Author : Raul Rojas
Publisher : Springer Science & Business Media
ISBN 13 : 3642610684
Total Pages : 511 pages
Book Rating : 4.84/5 ( download)

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Download or read book Neural Networks written by Raul Rojas and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : V. Rao Vemuri
Publisher :
ISBN 13 :
Total Pages : 168 pages
Book Rating : 4.32/5 ( download)

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Download or read book Artificial Neural Networks written by V. Rao Vemuri and published by . This book was released on 1988 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides an introduction to the field of artificial neural networks, and their role in the emerging field of neurocomputing, and the theoretical concepts that are the focus of current research. The genesis of this subject can be traced back to the 1940s, while present interest is due to recent developments in theoretical models, technologies, and algorithms. The papers selected for this volume were published primarily in IEEE journals.

Author : Bernhard Mehlig
Publisher : Cambridge University Press
ISBN 13 : 1108849563
Total Pages : 262 pages
Book Rating : 4.62/5 ( download)

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Download or read book Machine Learning with Neural Networks written by Bernhard Mehlig and published by Cambridge University Press. This book was released on 2021-10-28 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

Author : Subana Shanmuganathan
Publisher : Springer
ISBN 13 : 3319284959
Total Pages : 472 pages
Book Rating : 4.58/5 ( download)

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Download or read book Artificial Neural Network Modelling written by Subana Shanmuganathan and published by Springer. This book was released on 2016-02-03 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

Author : Leo P. J. Veelenturf
Publisher :
ISBN 13 :
Total Pages : 284 pages
Book Rating : 4.59/5 ( download)

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Download or read book Analysis and Applications of Artificial Neural Networks written by Leo P. J. Veelenturf and published by . This book was released on 1995 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is an analysis of the behaviour of the three types of neural networks: the binary perceptron, the continuous perceptron and the self-organizing neural network. Analysis is largely mathematical but concepts are also explained through practical examples.

Author : Robert J. Schalkoff
Publisher : McGraw-Hill Science, Engineering & Mathematics
ISBN 13 :
Total Pages : 456 pages
Book Rating : 4.81/5 ( download)

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Download or read book Artificial Neural Networks written by Robert J. Schalkoff and published by McGraw-Hill Science, Engineering & Mathematics. This book was released on 1997 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the primary objective of the text is to provide a teaching tool, practicing engineers and scientists are likely to find the clear, concept-based treatment useful in updating their backgrounds.

Author : Khalid Saeed
Publisher : Springer
ISBN 13 : 9783319453774
Total Pages : 754 pages
Book Rating : 4.77/5 ( download)

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Download or read book Computer Information Systems and Industrial Management written by Khalid Saeed and published by Springer. This book was released on 2016-09-09 with total page 754 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 15th IFIP TC8 International Conference on Computer Information Systems and Industrial Management, CISIM 2016, held in Vilnius, Lithuania, in September 2016. The 63 regular papers presented together with 1 inivted paper and 5 keynotes in this volume were carefully reviewed and selected from about 89 submissions. The main topics covered are rough set methods for big data analytics; images, visualization, classification; optimization, tuning; scheduling in manufacturing and other applications; algorithms; decisions; intelligent distributed systems; and biometrics, identification, security.