An Introduction To Neural Network Methods For Differential Equations
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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.
Artificial Neural Networks For Engineers And Scientists
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Author : S. Chakraverty
language : en
Publisher: CRC Press
Release Date : 2017-07-20
Artificial Neural Networks For Engineers And Scientists written by S. Chakraverty and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-20 with Mathematics categories.
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.
Semi Empirical Neural Network Modeling And Digital Twins Development
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Author : Dmitriy Tarkhov
language : en
Publisher: Academic Press
Release Date : 2019-11-23
Semi Empirical Neural Network Modeling And Digital Twins Development written by Dmitriy Tarkhov and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-23 with Science categories.
Semi-empirical Neural Network Modeling presents a new approach on how to quickly construct an accurate, multilayered neural network solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single-layered neural networks built on the finite element method (FEM). The strength of the new method presented in this book is the automatic inclusion of task parameters in the final solution formula, which eliminates the need for repeated problem-solving. This is especially important for constructing individual models with unique features. The book illustrates key concepts through a large number of specific problems, both hypothetical models and practical interest. - Offers a new approach to neural networks using a unified simulation model at all stages of design and operation - Illustrates this new approach with numerous concrete examples throughout the book - Presents the methodology in separate and clearly-defined stages
Artificial Neural Networks For Modelling And Control Of Non Linear Systems
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Author : Johan A.K. Suykens
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Artificial Neural Networks For Modelling And Control Of Non Linear Systems written by Johan A.K. Suykens 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.
Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLqemTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.
Artificial Intelligence And Soft Computing
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Author : Leszek Rutkowski
language : en
Publisher: Springer Nature
Release Date : 2021-10-04
Artificial Intelligence And Soft Computing written by Leszek Rutkowski and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-04 with Computers categories.
The two-volume set LNAI 12854 and 12855 constitutes the refereed proceedings of the 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021, held in Zakopane, Poland, in June 2021. Due to COVID 19, the conference was held virtually. The 89 full papers presented were carefully reviewed and selected from 195 submissions. The papers included both traditional artificial intelligence methods and soft computing techniques as well as follows: · Neural Networks and Their Applications · Fuzzy Systems and Their Applications · Evolutionary Algorithms and Their Applications · Artificial Intelligence in Modeling and Simulation · Computer Vision, Image and Speech Analysis · Data Mining · Various Problems of Artificial Intelligence · Bioinformatics, Biometrics and Medical Applications
Advances In Neural Networks Isnn 2016
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Author : Long Cheng
language : en
Publisher: Springer
Release Date : 2016-07-01
Advances In Neural Networks Isnn 2016 written by Long Cheng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-01 with Computers categories.
This book constitutes the refereed proceedings of the 13th International Symposium on Neural Networks, ISNN 2016, held in St. Petersburg, Russia in July 2016. The 84 revised full papers presented in this volume were carefully reviewed and selected from 104 submissions. The papers cover many topics of neural network-related research including signal and image processing; dynamical behaviors of recurrent neural networks; intelligent control; clustering, classification, modeling, and forecasting; evolutionary computation; and cognition computation and spiking neural networks.
Scientific Computing And Bioinformatics And Computational Biology
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Author : Douglas D. Hodson
language : en
Publisher: Springer Nature
Release Date : 2025-04-22
Scientific Computing And Bioinformatics And Computational Biology written by Douglas D. Hodson and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-22 with Computers categories.
This book constitutes the proceedings of the 22nd International Conference on Scientific Computing and Bioinformatics, CSC 2024, and the 25th International Conference on Computational Biology, BIOCOMP 2024, held as part of the 2024 World Congress in Computer Science, Computer Engineering and Applied Computing, in Las Vegas, USA, during July 22 to July 25, 2024. The proceedings include 25 papers from CSC 2024, which have been selected from a total of 128 submissions, and 27 papers from BIOCOMP 2024, that have been selected from 27 submissions. The papers have been organized in topical sections as follows: Military and defence modeling and simulation; scientific computing and applications; and bioinformatics and computational biology.
Cyber Physical Systems And Control Ii
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Author : Dmitry G. Arseniev
language : en
Publisher: Springer Nature
Release Date : 2023-01-20
Cyber Physical Systems And Control Ii written by Dmitry G. Arseniev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Technology & Engineering categories.
The book contains selected research papers presented at the 2nd International Conference on Cyber-Physical Systems and Control (CPS&C’2021) which was held from 29 June to 2 July 2021 in St. Petersburg, Russia. The CPS&C’2021 Conference continues the series of international conferences that began in 2019 when the first International Conference on Cyber-Physical Systems and Control (CPS&C’2019) took place. Cyber-physical systems (CPSs) considered a modern and rapidly emerging generation of systems with integrated wide computational, information processing, and physical capabilities that can interact with humans through many new modalities and application areas of implementation. The book covers the latest advances, developments and achievements in new theories, algorithms, models, and applications of prospective problems associated with CPSs with an emphasis on control theory and related areas. The multidisciplinary fundamental scientific and engineering principles that underpin the integration of cyber and physical elements across all application areas are discussed in the book chapters. The materials of the book may be of interest to scientists and engineers working in the field of cyber-physical systems, systems analysis, control systems, computer technologies, and similar fields.
Multivariate Statistical Machine Learning Methods For Genomic Prediction
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Author : Osval Antonio Montesinos López
language : en
Publisher: Springer Nature
Release Date : 2022-02-14
Multivariate Statistical Machine Learning Methods For Genomic Prediction written by Osval Antonio Montesinos López and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Technology & Engineering categories.
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.
Nature Inspired Computing
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Author : Bijaya Ketan Panigrahi
language : en
Publisher: Springer
Release Date : 2017-10-03
Nature Inspired Computing written by Bijaya Ketan Panigrahi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-03 with Computers categories.
This volume comprises the select proceedings of the annual convention of the Computer Society of India. Divided into 10 topical volumes, the proceedings present papers on state-of-the-art research, surveys, and succinct reviews. The volumes cover diverse topics ranging from communications networks to big data analytics, and from system architecture to cyber security. This volume focuses on Nature Inspired Computing. The contents of this book will be useful to researchers and students alike.