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Springer Fully Tuned RBF Neural Networks for Flight Control

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Description

Explore advanced adaptive control methods in Fully Tuned Radial Basis Function Neural Networks for Flight Control. Published by Springer as part of the International Series on Asian Studies in Computer and Information Science, this book focuses on the application of Radial Basis Function (RBF) neural networks for controlling nonlinear systems, specifically within flight control environments. This text introduces a Lyapunov synthesis approach to derive tuning rules for RBF controller parameters. This method is designed to guarantee the stability of the closed loop system. While many existing methods focus only on tuning the weights of the RBF network, this work provides a comprehensive derivation for tuning the centers, widths, and weights of the network. By addressing all three components, the book offers a more complete approach to neural network tuning for complex aerospace applications.

Key Features

Focuses on RBF neural networks for the adaptive control of nonlinear systems in flight control applications.

Uses a Lyapunov synthesis approach to derive specific tuning rules for RBF controller parameters.

Provides a method to guarantee the stability of the closed loop system during operation.

Includes the derivation of tuning laws for network centers, widths, and weights rather than just weights alone.

Part of the International Series on Asian Studies in Computer and Information Science (Volume 12).

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
07 December 2010
Listed Since
06 July 2010

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