Neural Network Modeling and Identification of Dynamical Systems

This book PDF is perfect for those who love Science genre, written by Yuri Tiumentsev and published by Academic Press which was released on 17 May 2019 with total hardcover pages 332. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Neural Network Modeling and Identification of Dynamical Systems books below.

Neural Network Modeling and Identification of Dynamical Systems
Neural Network Modeling and Identification of Dynamical Systems
Author : Yuri Tiumentsev
File Size : 51,7 Mb
Publisher : Academic Press
Language : English
Release Date : 17 May 2019
ISBN : 9780128154304
Pages : 332 pages
Get Book

Neural Network Modeling and Identification of Dynamical Systems by Yuri Tiumentsev Book PDF Summary

Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and training Offers application examples of dynamic neural network technologies, primarily related to aircraft Provides an overview of recent achievements and future needs in this area

Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of Dynamical Systems by Yuri Tiumentsev,Mikhail Egorchev

Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby

Get Book
Data Driven Science and Engineering

Data Driven Science and Engineering by Steven L. Brunton,J. Nathan Kutz

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Get Book
Identification of Dynamic Systems

Identification of Dynamic Systems by Rolf Isermann,Marco Münchhof

Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at

Get Book
Artificial Neural Networks for Modelling and Control of Non Linear Systems

Artificial Neural Networks for Modelling and Control of Non Linear Systems by Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor

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

Get Book
Neural Network Systems Techniques and Applications

Neural Network Systems Techniques and Applications by Anonim

The book emphasizes neural network structures for achieving practical and effective systems, and provides many examples. Practitioners, researchers, and students in industrial, manufacturing, electrical, mechanical,and production engineering will find this volume a unique and comprehensive reference source for diverse application methodologies. Control and Dynamic Systems covers the important topics

Get Book
Nonlinear System Identification

Nonlinear System Identification by Oliver Nelles

Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

Get Book
Neural Networks for Identification Prediction and Control

Neural Networks for Identification Prediction and Control by Duc T. Pham,Xing Liu

In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of

Get Book
Neural Networks for Modelling and Control of Dynamic Systems

Neural Networks for Modelling and Control of Dynamic Systems by M. Norgaard

Download or read online Neural Networks for Modelling and Control of Dynamic Systems written by M. Norgaard, published by Unknown which was released on 2003. Get Neural Networks for Modelling and Control of Dynamic Systems Books now! Available in PDF, ePub and Kindle.

Get Book