A learning algorithm for applying synthesized stable dynamics to system identification

Autor:	James W. Howse, Chaouki T. Abdallah, Gregory L. Heileman
Rok vydání:	1998
Předmět:	Lyapunov function Artificial neural network Cognitive Neuroscience Stability (learning theory) System identification symbols.namesake Dimension (vector space) Artificial Intelligence Bounded function Convergence (routing) Attractor symbols Algorithm Mathematics
Zdroj:	Neural Networks. 11:81-87
ISSN:	0893-6080
DOI:	10.1016/s0893-6080(97)00109-3
Popis:	In this paper the models discussed by Cohen are extended by introducing an input term. This allows the resulting models to be utilized for system identification tasks. This approach gives a direct way to encode qualitative information such as attractor dimension into the model. We prove that this model is stable in the sense that a bounded input leads to a bounded state when a minor restriction is imposed on the Lyapunov function. By employing this stability result, we are able to find a learning algorithm which guarantees convergence to a set of parameters for which the error between the model trajectories and the desired trajectories vanishes.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99b5b45193dcaaba0c1dfb8ffc7434f1 https://doi.org/10.1016/s0893-6080(97)00109-3 Zobrazit plný text záznamu Full Text from ScienceDirect