A systematic synthesis of a neural network-based smoother

Autor:	H.T. Toivonen, A.V. Medvedev
Rok vydání:	2003
Předmět:	Engineering Signal processing Artificial neural network business.industry Control theory Process (computing) State vector Feedforward neural network Fuzzy control system White noise Network synthesis filters business
Zdroj:	Proceedings of the 1992 IEEE International Symposium on Intelligent Control.
DOI:	10.1109/isic.1992.225083
Popis:	A feedforward neural network (FNN) implementation of a finite-memory smoother (FMS) is proposed. For a linear time-invariant dynamic system with measurement and process white noise, a single-layer FNN with delayed inputs is found to possess the same structure as the FMS designed by the least-squares method. The FNN-based FMS features definite speed advantages over conventional approaches and intrinsically finite process memory. Due to its parallel structure and absence of state vector integration, the FNN suffices for real-time applications. A numerical example illustrates the design procedure. >
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::869fdbafed3376891c87c148ff1775ca https://doi.org/10.1109/isic.1992.225083 Zobrazit plný text záznamu