Applied Artificial Neural Network Methods For Engineers And Scientists Solving Algebraic Equations

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Applied Artificial Neural Network Methods For Engineers And Scientists: Solving Algebraic Equations

Snehashish Chakraverty,Sumit Kumar Jeswal
Applied Artificial Neural Network Methods For Engineers And Scientists Solving Algebraic Equations Book PDF

Author : Snehashish Chakraverty,Sumit Kumar Jeswal
Publisher : World Scientific
Page : 192 pages
File Size : 49,6 Mb
Release : 2021-01-26
Category : Computers
ISBN : 9789811230226

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Applied Artificial Neural Network Methods For Engineers And Scientists: Solving Algebraic Equations by Snehashish Chakraverty,Sumit Kumar Jeswal Pdf

The aim of this book is to handle different application problems of science and engineering using expert Artificial Neural Network (ANN). As such, the book starts with basics of ANN along with different mathematical preliminaries with respect to algebraic equations. Then it addresses ANN based methods for solving different algebraic equations viz. polynomial equations, diophantine equations, transcendental equations, system of linear and nonlinear equations, eigenvalue problems etc. which are the basic equations to handle the application problems mentioned in the content of the book. Although there exist various methods to handle these problems, but sometimes those may be problem dependent and may fail to give a converge solution with particular discretization. Accordingly, ANN based methods have been addressed here to solve these problems. Detail ANN architecture with step by step procedure and algorithm have been included. Different example problems are solved with respect to various application and mathematical problems. Convergence plots and/or convergence tables of the solutions are depicted to show the efficacy of these methods. It is worth mentioning that various application problems viz. Bakery problem, Power electronics applications, Pole placement, Electrical Network Analysis, Structural engineering problem etc. have been solved using the ANN based methods.

Artificial Neural Networks for Engineers and Scientists

Snehashish Chakraverty,Susmita Mall
Artificial Neural Networks for Engineers and Scientists Book PDF

Author : Snehashish Chakraverty,Susmita Mall
Publisher : Unknown
Page : 150 pages
File Size : 49,9 Mb
Release : 2017
Category : Artificial intelligence
ISBN : 1351641808

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Artificial Neural Networks for Engineers and Scientists by Snehashish Chakraverty,Susmita Mall Pdf

"Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solutions of differential equations may not be obtained easily, so numerical methods have been developed to handle them. Machine intelligence methods, such as Artificial Neural Networks (ANN), are being used to solve differential equations, and these methods are presented in Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations. This book shows how computation of differential equation becomes faster once the ANN model is properly developed and applied."--Provided by publisher.

An Introduction to Neural Network Methods for Differential Equations

Neha Yadav,Anupam Yadav,Manoj Kumar
An Introduction to Neural Network Methods for Differential Equations Book PDF

Author : Neha Yadav,Anupam Yadav,Manoj Kumar
Publisher : Springer
Page : 114 pages
File Size : 43,8 Mb
Release : 2015-02-26
Category : Mathematics
ISBN : 9789401798167

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An Introduction to Neural Network Methods for Differential Equations by Neha Yadav,Anupam Yadav,Manoj Kumar Pdf

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.

Artificial Neural Networks for Engineers and Scientists

Snehashish Chakraverty,Susmita Mall
Artificial Neural Networks for Engineers and Scientists Book PDF

Author : Snehashish Chakraverty,Susmita Mall
Publisher : CRC Press
Page : 168 pages
File Size : 52,9 Mb
Release : 2017-08-03
Category : Electronic
ISBN : 113874784X

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Artificial Neural Networks for Engineers and Scientists by Snehashish Chakraverty,Susmita Mall Pdf

Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solutions of differential equations may not be obtained easily, so numerical methods have been developed to handle them. Machine intelligence methods, such as Artificial Neural Networks (ANN), are being used to solve differential equations, and these methods are presented in Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations. This book shows how computation of differential equation becomes faster once the ANN model is properly developed and applied.

Mathematical Methods in Dynamical Systems

S. Chakraverty,Subrat Kumar Jena
Mathematical Methods in Dynamical Systems Book PDF

Author : S. Chakraverty,Subrat Kumar Jena
Publisher : CRC Press
Page : 508 pages
File Size : 41,9 Mb
Release : 2023-05-19
Category : Mathematics
ISBN : 9781000833805

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Mathematical Methods in Dynamical Systems by S. Chakraverty,Subrat Kumar Jena Pdf

The art of applying mathematics to real-world dynamical problems such as structural dynamics, fluid dynamics, wave dynamics, robot dynamics, etc. can be extremely challenging. Various aspects of mathematical modelling that may include deterministic or uncertain (fuzzy, interval, or stochastic) scenarios, along with integer or fractional order, are vital to understanding these dynamical systems. Mathematical Methods in Dynamical Systems offers problem-solving techniques and includes different analytical, semi-analytical, numerical, and machine intelligence methods for finding exact and/or approximate solutions of governing equations arising in dynamical systems. It provides a singular source of computationally efficient methods to investigate these systems and includes coverage of various industrial applications in a simple yet comprehensive way.

Artificial Neural Networks for Engineers and Scientists

S. Chakraverty,Susmita Mall
Artificial Neural Networks for Engineers and Scientists Book PDF

Author : S. Chakraverty,Susmita Mall
Publisher : CRC Press
Page : 156 pages
File Size : 55,6 Mb
Release : 2017-07-20
Category : Mathematics
ISBN : 9781351651318

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Artificial Neural Networks for Engineers and Scientists by S. Chakraverty,Susmita Mall Pdf

Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solutions of differential equations may not be obtained easily, so numerical methods have been developed to handle them. Machine intelligence methods, such as Artificial Neural Networks (ANN), are being used to solve differential equations, and these methods are presented in Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations. This book shows how computation of differential equation becomes faster once the ANN model is properly developed and applied.

Applied Artificial Neural Networks

Christian Dawson
Applied Artificial Neural Networks Book PDF

Author : Christian Dawson
Publisher : MDPI
Page : 259 pages
File Size : 41,9 Mb
Release : 2018-09-27
Category : Electronic book
ISBN : 9783038422709

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Applied Artificial Neural Networks by Christian Dawson Pdf

This book is a printed edition of the Special Issue "Applied Artificial Neural Network" that was published in Applied Sciences

Computational Mechanics with Neural Networks

Genki Yagawa,Atsuya Oishi
Computational Mechanics with Neural Networks Book PDF

Author : Genki Yagawa,Atsuya Oishi
Publisher : Springer Nature
Page : 233 pages
File Size : 54,7 Mb
Release : 2021-02-26
Category : Technology & Engineering
ISBN : 9783030661113

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Computational Mechanics with Neural Networks by Genki Yagawa,Atsuya Oishi Pdf

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering

Nikola K. Kasabov
Foundations of Neural Networks Fuzzy Systems and Knowledge Engineering Book PDF

Author : Nikola K. Kasabov
Publisher : Marcel Alencar
Page : 581 pages
File Size : 49,8 Mb
Release : 1996
Category : Artificial intelligence
ISBN : 9780262112123

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Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering by Nikola K. Kasabov Pdf

Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.

Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications

Zhang, Ming
Artificial Higher Order Neural Networks for Computer Science and Engineering Trends for Emerging Applications Book PDF

Author : Zhang, Ming
Publisher : IGI Global
Page : 660 pages
File Size : 53,5 Mb
Release : 2010-02-28
Category : Computers
ISBN : 9781615207121

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Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications by Zhang, Ming Pdf

"This book introduces and explains Higher Order Neural Networks (HONNs) to people working in the fields of computer science and computer engineering, and how to use HONNS in these areas"--Provided by publisher.

Artificial Neural Nets. Problem Solving Methods

José Mira,José R. Alvarez
Artificial Neural Nets Problem Solving Methods Book PDF

Author : José Mira,José R. Alvarez
Publisher : Springer
Page : 828 pages
File Size : 41,6 Mb
Release : 2003-08-03
Category : Computers
ISBN : 9783540448693

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Artificial Neural Nets. Problem Solving Methods by José Mira,José R. Alvarez Pdf

The two-volume set LNCS 2686 and LNCS 2687 constitute the refereed proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, held in MaÃ3, Menorca, Spain in June 2003.The 197 revised papers presented were carefully reviewed and selected for inclusion in the book and address the following topics: mathematical and computational methods in neural modelling, neurophysiological data analysis and modelling, structural and functional models of neurons, learning and other plasticity phenomena, complex systems dynamics, cognitive processes and artificial intelligence, methodologies for net design, bio-inspired systems and engineering, and applications in a broad variety of fields.nbsp;

Principles of Artificial Neural Networks

Daniel Graupe
Principles of Artificial Neural Networks Book PDF

Author : Daniel Graupe
Publisher : World Scientific
Page : 500 pages
File Size : 48,5 Mb
Release : 2013-07-31
Category : Computers
ISBN : 9789814522755

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Principles of Artificial Neural Networks by Daniel Graupe Pdf

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 — 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. Contents:Introduction and Role of Artificial Neural NetworksFundamentals of Biological Neural NetworksBasic Principles of ANNs and Their Early StructuresThe PerceptronThe MadalineBack PropagationHopfield NetworksCounter PropagationLarge Scale Memory Storage and Retrieval (LAMSTAR) NetworkAdaptive Resonance TheoryThe Cognitron and the NeocognitronStatistical TrainingRecurrent (Time Cycling) Back Propagation Networks Readership: Graduate and advanced senior students in artificial intelligence, pattern recognition & image analysis, neural networks, computational economics and finance, and biomedical engineering. Keywords:Neural Networks;Mathematical Derivations;Source Codes;Medical Applications;Data Mining;Cell-Shape Recognition;Micro-Trading

A Design and Development Method for Artificial Neural Network Projects

Stefan Vogt
A Design and Development Method for Artificial Neural Network Projects Book PDF

Author : Stefan Vogt
Publisher : diplom.de
Page : 138 pages
File Size : 40,8 Mb
Release : 2000-01-03
Category : Computers
ISBN : 9783832420215

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A Design and Development Method for Artificial Neural Network Projects by Stefan Vogt Pdf

Inhaltsangabe:Abstract: In the 1980s research efforts and successes made artificial neural networks popular. Since the 1990s engineers have been using this foundation for problem solving. But artifiial neural network solutions for "real-world" problems are sometimes hard to find because of the complexity of the domain and because of the vast number of design attributes the engineer has to deal with. This thesis provides a structured overview of attributes in the design process of artificial neural networks and reviews technical process models. Current development methods for artificial neural networks are then reviewed and critiqued. The thesis concludes with a new design and development method for artificial neural networks. Inhaltsverzeichnis:Table of Contents: List of figuresx List of tablesxi Introduction1 1.Design attributes in ANN3 1.1ANN models4 1.1.1Node level7 1.1.2Network level9 1.1.3Training level9 1.2Data and data representation10 1.3Global system design12 1.4Hardware and software implementation13 1.5Characteristics of ANNs15 1.5.1Advantages of ANNs15 1.5.2Limitations and concerns16 2.Technical process models and engineering methods18 2.1Why use an engineering method?18 2.2Evolutionary model of engineering discipline20 2.3Overview of technical process models22 2.3.1Taxonomy of technical process models24 2.3.2Prototyping25 2.3.3Incremental method26 2.3.4Strict contractual approach26 2.3.5Deciding on process models and methods26 2.3.6Examples of process models27 2.3.7Representation of process models27 2.4Quality criteria of process models29 3.Current engineering methods for ANNs30 3.1Why a special method for ANNs?30 3.1.1Are conventional engineering methodologies suitable for ANNs?30 3.2Methods for expert systems31 3.3System identication methods35 3.4Bailey and Thompson37 3.4.1Criticism43 3.5Medsker and Liebowitz44 3.6Jones and Franklin45 3.7Schalko47 3.8Karayiannis and Nicolaos48 3.8.1Criticism49 3.9Nelson and Illingworth50 3.9.1Criticism51 3.10Whittington and Spracklen52 3.10.1Criticism56 3.11Lawrence and Andriola57 3.11.1Criticism58 3.12General criticism of current methodologies58 4.Proposed design and development method60 4.1Development process61 4.1.1Requirement analysis65 4.1.2Specication68 4.1.3Data and domain analysis70 4.1.4Architectural design76 4.1.5Detailed ANN design84 4.1.6ANN implementation92 4.1.7Training93 4.1.8Monitoring training94 4.1.9ANN quality [...]

Neural Networks for Applied Sciences and Engineering

Sandhya Samarasinghe
Neural Networks for Applied Sciences and Engineering Book PDF

Author : Sandhya Samarasinghe
Publisher : Auerbach Publications
Page : 570 pages
File Size : 51,5 Mb
Release : 2006-09-12
Category : Computers
ISBN : 084933375X

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Neural Networks for Applied Sciences and Engineering by Sandhya Samarasinghe Pdf

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.

A Matrix Algebra Approach to Artificial Intelligence

Xian-Da Zhang
A Matrix Algebra Approach to Artificial Intelligence Book PDF

Author : Xian-Da Zhang
Publisher : Springer Nature
Page : 844 pages
File Size : 47,7 Mb
Release : 2020-05-23
Category : Computers
ISBN : 9789811527708

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A Matrix Algebra Approach to Artificial Intelligence by Xian-Da Zhang Pdf

Matrix algebra plays an important role in many core artificial intelligence (AI) areas, including machine learning, neural networks, support vector machines (SVMs) and evolutionary computation. This book offers a comprehensive and in-depth discussion of matrix algebra theory and methods for these four core areas of AI, while also approaching AI from a theoretical matrix algebra perspective. The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines, including, but not limited to, computer science, mathematics and engineering.