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Neural network methods for dynamic e...

Georgiev, Svetlin.

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Neural network methods for dynamic equations on time scales

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural network methods for dynamic equations on time scales/ by Svetlin Georgiev.
Author:	Georgiev, Svetlin.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	viii, 112 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Introduction -- Multilayer Artificial Neural Networks -- Regression Based Artificial Neural Networks -- Chebyshev Neural Networks -- Legendre Neural Networks -- Index.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-031-85056-1
ISBN:	9783031850561

Neural network methods for dynamic equations on time scales
Georgiev, Svetlin.

Neural network methods for dynamic equations on time scales[electronic resource] /by Svetlin Georgiev. - Cham :Springer Nature Switzerland :2025. - viii, 112 p. :ill. (chiefly color), digital ;24 cm. - SpringerBriefs in applied sciences and technology. Computational intelligence,2625-3712. - SpringerBriefs in applied sciences and technology.Computational intelligence..

Introduction -- Multilayer Artificial Neural Networks -- Regression Based Artificial Neural Networks -- Chebyshev Neural Networks -- Legendre Neural Networks -- Index.

This book aims to handle dynamic equations on time scales using artificial neural network (ANN). Basic facts and methods for ANN modeling are considered. The multilayer artificial neural network (ANN) model is introduced for solving of dynamic equations on arbitrary time scales. A multilayer ANN model with one input layer containing a single node, a hidden layer with m nodes, and one output node are investigated. The feed-forward neural network model and unsupervised error back-propagation algorithm are developed. Modification of network parameters is done without the use of any optimization technique. The regression-based neural network (RBNN) model is introduced for solving dynamic equations on arbitrary time scales. The RBNN trial solution of dynamic equations is obtained by using the RBNN model for single input and single output system. A variety of initial and boundary value problems are solved. The Chebyshev neural network (ChNN) model and Levendre neural network model are developed. The ChNN trial solution of dynamic equations is obtained by using the ChNN model for single input and single output system. This book is addressed to a wide audience of specialists such as mathematicians, physicists, engineers, and biologists. It can be used as a textbook at the graduate level and as a reference book for several disciplines.

ISBN: 9783031850561

Standard No.: 10.1007/978-3-031-85056-1doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Neural network methods for dynamic equations on time scales
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520 $a This book aims to handle dynamic equations on time scales using artificial neural network (ANN). Basic facts and methods for ANN modeling are considered. The multilayer artificial neural network (ANN) model is introduced for solving of dynamic equations on arbitrary time scales. A multilayer ANN model with one input layer containing a single node, a hidden layer with m nodes, and one output node are investigated. The feed-forward neural network model and unsupervised error back-propagation algorithm are developed. Modification of network parameters is done without the use of any optimization technique. The regression-based neural network (RBNN) model is introduced for solving dynamic equations on arbitrary time scales. The RBNN trial solution of dynamic equations is obtained by using the RBNN model for single input and single output system. A variety of initial and boundary value problems are solved. The Chebyshev neural network (ChNN) model and Levendre neural network model are developed. The ChNN trial solution of dynamic equations is obtained by using the ChNN model for single input and single output system. This book is addressed to a wide audience of specialists such as mathematicians, physicists, engineers, and biologists. It can be used as a textbook at the graduate level and as a reference book for several disciplines.
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