๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

Fully Tuned Radial Basis Function Neural Networks for Flight Control

โœ Scribed by N. Sundararajan, P. Saratchandran, Yan Li (auth.)


Publisher
Springer US
Year
2002
Tongue
English
Leaves
166
Series
The Springer International Series on Asian Studies in Computer and Information Science 12
Edition
1
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


Fully Tuned Radial Basis Function Neural Networks for Flight Control presents the use of the Radial Basis Function (RBF) neural networks for adaptive control of nonlinear systems with emphasis on flight control applications. A Lyapunov synthesis approach is used to derive the tuning rules for the RBF controller parameters in order to guarantee the stability of the closed loop system. Unlike previous methods that tune only the weights of the RBF network, this book presents the derivation of the tuning law for tuning the centers, widths, and weights of the RBF network, and compares the results with existing algorithms. It also includes a detailed review of system identification, including indirect and direct adaptive control of nonlinear systems using neural networks.
Fully Tuned Radial Basis Function Neural Networks for Flight Control is an excellent resource for professionals using neural adaptive controllers for flight control applications.

โœฆ Table of Contents


Front Matter....Pages i-xv
A Review of Nonlinear Adaptive Neural Control Schemes....Pages 1-24
Front Matter....Pages 25-28
Nonlinear System Identification Using Lyapunov-Based Fully Tuned RBFN....Pages 29-45
Real-Time Identification of Nonlinear Systems Using MRAN/EMRAN Algorithm....Pages 47-68
Indirect Adaptive Control Using Fully Tuned RBFN....Pages 69-80
Front Matter....Pages 81-83
Direct Adaptive Neuro Flight Controller Using Fully Tuned RBFN....Pages 85-94
Aircraft Flight Control Applications Using Direct Adaptive NFC....Pages 95-125
MRAN Neuro-Flight-Controller for Robust Aircraft Control....Pages 127-140
Conclusions and Future Work....Pages 141-144
Back Matter....Pages 145-158

โœฆ Subjects


Statistical Physics, Dynamical Systems and Complexity;Calculus of Variations and Optimal Control;Optimization;Artificial Intelligence (incl. Robotics);Automotive Engineering


๐Ÿ“œ SIMILAR VOLUMES


Radial Basis Function (RBF) Neural Netwo
โœ Jinkun Liu (auth.) ๐Ÿ“‚ Library ๐Ÿ“… 2013 ๐Ÿ› Springer-Verlag Berlin Heidelberg ๐ŸŒ English

<p><p><b><i>Radial Basis</i></b><b><i> Function (RBF)</i></b><b><i> Neural Network Control</i></b><b><i>for Mechanical Systems</i></b> is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main obje

Neural networks for control
โœ W Thomas Miller; Richard S Sutton; Paul J Werbos; National Science Foundation ( ๐Ÿ“‚ Library ๐Ÿ“… 1990 ๐Ÿ› MIT Press ๐ŸŒ English
Intelligent Control Based on Flexible Ne
โœ Mohammad Teshnehlab, Keigo Watanabe (auth.) ๐Ÿ“‚ Library ๐Ÿ“… 1999 ๐Ÿ› Springer Netherlands ๐ŸŒ English

<p>References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Chapter 3 Flexible Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . 61 3. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The Neural Basis of Oral and Facial Func
โœ Ronald Dubner D. D. S., Ph. D., Barry J. Sessle M. D. S., Ph. D., Arthur T. Stor ๐Ÿ“‚ Library ๐Ÿ“… 1978 ๐Ÿ› Springer US ๐ŸŒ English

<p>This book is a result of our combined major interests in oral and facial function. Since most of our research efforts have been concentrated on fundamental neural mechanisms, the book emphasizes basic research in this area. However, our backยญ grounds in clinical dentistry have always made us acut

Radial Basis Function Networks 2: New Ad
โœ J. Ghosh, A. Nag (auth.), Dr. Robert J. Howlett, Professor Lakhmi C. Jain (eds.) ๐Ÿ“‚ Library ๐Ÿ“… 2001 ๐Ÿ› Physica-Verlag Heidelberg ๐ŸŒ English

<p>The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the