Hybrid differential neural network identifier for partially uncertain hybrid systems

Alejandro García; Isaac Chairez; Alexander Poznyak

doi:10.1007/978-1-84882-548-2_7

Hybrid differential neural network identifier for partially uncertain hybrid systems

Alejandro García, Isaac Chairez, Alexander Poznyak

Unidad Profesional Interdisciplinaria de Biotecnología (UPIBI)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

11 Scopus citations

Abstract

This chapter presents a hybrid differential neural network (DNN)-identifier has demonstrated excellent results even in the presence of perturbations. Convergence analysis is realized considering the practical stability of identification error for a general class of hybrid systems. As can be seen in the numerical examples this algorithm could be easily implemented. In this sense the artificial modeling strategy of the continuous subsystems could be used in the automatic control implementation.

Original language	English
Title of host publication	Recent Advances in Intelligent Control Systems
Publisher	Springer London
Pages	149-168
Number of pages	20
ISBN (Print)	9781848825475
DOIs	https://doi.org/10.1007/978-1-84882-548-2_7
State	Published - 2009

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10.1007/978-1-84882-548-2_7

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title = "Hybrid differential neural network identifier for partially uncertain hybrid systems",

abstract = "This chapter presents a hybrid differential neural network (DNN)-identifier has demonstrated excellent results even in the presence of perturbations. Convergence analysis is realized considering the practical stability of identification error for a general class of hybrid systems. As can be seen in the numerical examples this algorithm could be easily implemented. In this sense the artificial modeling strategy of the continuous subsystems could be used in the automatic control implementation.",

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AU - Chairez, Isaac

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AB - This chapter presents a hybrid differential neural network (DNN)-identifier has demonstrated excellent results even in the presence of perturbations. Convergence analysis is realized considering the practical stability of identification error for a general class of hybrid systems. As can be seen in the numerical examples this algorithm could be easily implemented. In this sense the artificial modeling strategy of the continuous subsystems could be used in the automatic control implementation.

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BT - Recent Advances in Intelligent Control Systems

PB - Springer London

ER -

Hybrid differential neural network identifier for partially uncertain hybrid systems

Abstract

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