Generalized backpropagation through time for continuous time neural networks and discrete time measurements

Krzysztof Fujarewicz; Adam Galuszka

doi:10.1007/978-3-540-24844-6_24

Generalized backpropagation through time for continuous time neural networks and discrete time measurements

Wyniki badań: Wkład do czasopisma › Artykuł z konferencji › recenzja

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Abstrakt

This paper deals with the problem of identification of continuous time dynamic neural networks when the measurements are given only at discrete time moments, not necessarily uniformly distributed. It is shown that the modified adjoint system, generating the gradient of the performance index, is a continuous-time system with jumps of state variables at moments corresponding to moments of measurements.

Język oryginału	angielski
Strony (od–do)	190-196
Liczba stron	7
Czasopismo	Lecture Notes in Computer Science
Tom	3070
Identyfikatory DOI	https://doi.org/10.1007/978-3-540-24844-6_24
Status publikacji	Opublikowano - 2004
Wydarzenie	7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004 - Zakopane, Polska Czas trwania: 7 cze 2004 → 11 cze 2004

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Informatyka teoretyczna
Informatyka ogólna

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10.1007/978-3-540-24844-6_24

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title = "Generalized backpropagation through time for continuous time neural networks and discrete time measurements",

abstract = "This paper deals with the problem of identification of continuous time dynamic neural networks when the measurements are given only at discrete time moments, not necessarily uniformly distributed. It is shown that the modified adjoint system, generating the gradient of the performance index, is a continuous-time system with jumps of state variables at moments corresponding to moments of measurements.",

author = "Krzysztof Fujarewicz and Adam Galuszka",

year = "2004",

doi = "10.1007/978-3-540-24844-6\_24",

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journal = "Lecture Notes in Computer Science",

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AU - Fujarewicz, Krzysztof

AU - Galuszka, Adam

PY - 2004

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N2 - This paper deals with the problem of identification of continuous time dynamic neural networks when the measurements are given only at discrete time moments, not necessarily uniformly distributed. It is shown that the modified adjoint system, generating the gradient of the performance index, is a continuous-time system with jumps of state variables at moments corresponding to moments of measurements.

AB - This paper deals with the problem of identification of continuous time dynamic neural networks when the measurements are given only at discrete time moments, not necessarily uniformly distributed. It is shown that the modified adjoint system, generating the gradient of the performance index, is a continuous-time system with jumps of state variables at moments corresponding to moments of measurements.

UR - https://www.scopus.com/pages/publications/9444261975

U2 - 10.1007/978-3-540-24844-6_24

DO - 10.1007/978-3-540-24844-6_24

M3 - Conference article

AN - SCOPUS:9444261975

SN - 0302-9743

VL - 3070

SP - 190

EP - 196

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004

Y2 - 7 June 2004 through 11 June 2004

ER -

Generalized backpropagation through time for continuous time neural networks and discrete time measurements

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