Feedforward Neural Network Methodology
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Feedforward Neural Network Methodology
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Author : Terrence L. Fine
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-06
Feedforward Neural Network Methodology written by Terrence L. Fine and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-06 with Computers categories.
This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.
Feed Forward Neural Networks
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Author : Jouke Annema
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Feed Forward Neural Networks written by Jouke Annema and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips. Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation. Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.
Neural Networks
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Author : Gérard Dreyfus
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-11-25
Neural Networks written by Gérard Dreyfus and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-25 with Science categories.
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Process Neural Networks
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Author : Xingui He
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-05
Process Neural Networks written by Xingui He and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-05 with Computers categories.
For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.
Feedforward Neural Networks
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-06-24
Feedforward Neural Networks written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-24 with Computers categories.
What Is Feedforward Neural Networks A feedforward neural network, often known as a FNN, is a type of artificial neural network that does not have connections that form a cycle between its nodes. Therefore, it is distinct from its offspring, which are known as recurrent neural networks. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Feedforward neural network Chapter 2: Artificial neural network Chapter 3: Perceptron Chapter 4: Artificial neuron Chapter 5: Multilayer perceptron Chapter 6: Delta rule Chapter 7: Backpropagation Chapter 8: Types of artificial neural networks Chapter 9: Learning rule Chapter 10: Mathematics of artificial neural networks (II) Answering the public top questions about feedforward neural networks. (III) Real world examples for the usage of feedforward neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of feedforward neural networks. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Sequences Of Near Optimal Feedforward Neural Networks
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Author : Pramod Lakshmi Narasimha
language : en
Publisher: ProQuest
Release Date : 2007
Sequences Of Near Optimal Feedforward Neural Networks written by Pramod Lakshmi Narasimha and has been published by ProQuest this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Electrical engineering categories.
In order to facilitate complexity optimization in feedforward networks, several inte- grated growing and pruning algorithms are developed. First, a growing scheme is reviewed which iteratively adds new hidden units to full-trained networks. Then, a non-heuristic one-pass pruning technique is reviewed, which utilizes orthogonal least squares. Based upon pruning, a one-pass approach is developed for producing the validation error versus network size curve. Then, a combined approach is devised in which grown networks are pruned. As a result, the hidden units are ordered according to their usefulness, and less useful units are eliminated. In several examples, it is shown that networks designed using the integrated growing and pruning method have less training and validation error. This combined method exhibits reduced sensitivity to the choice of the initial weights and produces an almost monotonic error versus network size curve. Starting from the strict interpolation equations for multivariate polynomials, an upper bound is developed for the number of patterns that can be memorized by a non-linear feedforward network. A straightforward proof by contradiction is presented for the upper bound. It is shown that the hidden activations do not have to be analytic. Networks, trained by conjugate gradient, are used to demonstrate the tightness of the bound for random patterns. The theoretical results agree closely to the simulations on two class problems solved by support vector machines. We model large classifiers like Support Vector Machines (SVMs) by smaller networks in order to decrease the computational cost. The key idea is to generate additional training patterns using a trained SVM and use these additional patterns along with the original training patterns to train a much smaller neural network. Results shown verify the validity of the technique and the method used to generate additional patterns. We also generalize this idea and prove that any learning machine can be used to generate additional patterns and in turn train any other machine to improve its performance.
An Introduction To Neural Network Methods For Differential Equations
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Author : Neha Yadav
language : en
Publisher: Springer
Release Date : 2015-02-26
An Introduction To Neural Network Methods For Differential Equations written by Neha Yadav and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-26 with Mathematics categories.
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.
Neural Networks
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Author : Richard J. Mammone
language : en
Publisher:
Release Date : 1991
Neural Networks written by Richard J. Mammone and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.
Neural networks have attracted the interest of scientists from many disciplines: engineering, computer science, mathematics, physics, biology, and cognitive science. This volume collects 15 contributions written by leading international researchers that illustrate important features of various neural network methodologies. Topics discussed include the fundamental principles of neural networks and various modifications of basic neural systems that improve system performance in specific application domains. Where appropriate, improvements are demonstrated by numerical examples.
Nonlinear Dynamical Systems
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Author : Irwin W. Sandberg
language : en
Publisher: John Wiley & Sons
Release Date : 2001-02-21
Nonlinear Dynamical Systems written by Irwin W. Sandberg and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-02-21 with Technology & Engineering categories.
Sechs erfahrene Autoren beschreiben in diesem Band ein Spezialgebiet der neuronalen Netze mit Anwendungen in der Signalsteuerung, Signalverarbeitung und Zeitreihenanalyse. Ein zeitgemäßer Beitrag zur Behandlung nichtlinear-dynamischer Systeme!
Feed Forward Neural Networks
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Author : Jouke Annema
language : en
Publisher: Springer
Release Date : 2013-07-13
Feed Forward Neural Networks written by Jouke Annema and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-13 with Technology & Engineering categories.
Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips. Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation. Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.