Название: Semi-empirical Neural Network Modeling and Digital Twins Development Автор: Dmitriy Tarkhov, Alexander Vasilyev
Издательство: Academic Press
Год: 2020
Страниц: 281
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
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.
Industry 4.0, the transition to which is currently underway, is not possible without solving the above problems. However, presently accepted methods of mathematical modeling are poorly adapted to their solution. In our opinion, the creation of suitable mathematical and algorithmic tools to solve uniformly a wide range of problems arising in the transition to Industry 4.0 is a crucial step in the development of Industry 4.1. By Industry 4.1, we mean an industry that has the same production processes as Industry 4.0, but the implementation of these processes will be based on unified and cheaper technologies.
We believe that one of these key technologies is the neural network technique. Currently, neural networks are actively used in the problems of Big Data, image processing, pattern recognition, complex system control, and other tasks of artificial intelligence. The issue of creating cyber-physical systems requires adequate means of mathematical modeling. We offer neural networks as such tools. Quite a lot of publications is devoted to the application of neural networks to mathematical modeling problems; a small review of them we have led in the introduction to this book. At the moment, it is necessary to move from solving individual problems to a single methodology for solving them, to which we have devoted this monograph. As the main class of mathematical models, the class of artificial neural networks (ANN) is used, that have proven themselves to behave well in complex data processing tasks. At the moment, the neural network technique is one of the most dynamically developing spheres of Artificial Intelligence (AI).
Key Features:
- 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
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