Matches in SemOpenAlex for { <https://semopenalex.org/work/W798740310> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W798740310 abstract "A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators." @default.
- W798740310 created "2016-06-24" @default.
- W798740310 creator A5013978386 @default.
- W798740310 creator A5017676848 @default.
- W798740310 creator A5020040330 @default.
- W798740310 date "2005-05-01" @default.
- W798740310 modified "2023-09-23" @default.
- W798740310 title "Adaptive Filtering Using Recurrent Neural Networks" @default.
- W798740310 hasPublicationYear "2005" @default.
- W798740310 type Work @default.
- W798740310 sameAs 798740310 @default.
- W798740310 citedByCount "1" @default.
- W798740310 crossrefType "journal-article" @default.
- W798740310 hasAuthorship W798740310A5013978386 @default.
- W798740310 hasAuthorship W798740310A5017676848 @default.
- W798740310 hasAuthorship W798740310A5020040330 @default.
- W798740310 hasConcept C106131492 @default.
- W798740310 hasConcept C111919701 @default.
- W798740310 hasConcept C11210021 @default.
- W798740310 hasConcept C112633086 @default.
- W798740310 hasConcept C11413529 @default.
- W798740310 hasConcept C115961682 @default.
- W798740310 hasConcept C121332964 @default.
- W798740310 hasConcept C154945302 @default.
- W798740310 hasConcept C157286648 @default.
- W798740310 hasConcept C158622935 @default.
- W798740310 hasConcept C206833254 @default.
- W798740310 hasConcept C2775924081 @default.
- W798740310 hasConcept C31972630 @default.
- W798740310 hasConcept C41008148 @default.
- W798740310 hasConcept C47446073 @default.
- W798740310 hasConcept C50644808 @default.
- W798740310 hasConcept C62520636 @default.
- W798740310 hasConcept C76155785 @default.
- W798740310 hasConcept C8639503 @default.
- W798740310 hasConcept C98045186 @default.
- W798740310 hasConcept C99498987 @default.
- W798740310 hasConceptScore W798740310C106131492 @default.
- W798740310 hasConceptScore W798740310C111919701 @default.
- W798740310 hasConceptScore W798740310C11210021 @default.
- W798740310 hasConceptScore W798740310C112633086 @default.
- W798740310 hasConceptScore W798740310C11413529 @default.
- W798740310 hasConceptScore W798740310C115961682 @default.
- W798740310 hasConceptScore W798740310C121332964 @default.
- W798740310 hasConceptScore W798740310C154945302 @default.
- W798740310 hasConceptScore W798740310C157286648 @default.
- W798740310 hasConceptScore W798740310C158622935 @default.
- W798740310 hasConceptScore W798740310C206833254 @default.
- W798740310 hasConceptScore W798740310C2775924081 @default.
- W798740310 hasConceptScore W798740310C31972630 @default.
- W798740310 hasConceptScore W798740310C41008148 @default.
- W798740310 hasConceptScore W798740310C47446073 @default.
- W798740310 hasConceptScore W798740310C50644808 @default.
- W798740310 hasConceptScore W798740310C62520636 @default.
- W798740310 hasConceptScore W798740310C76155785 @default.
- W798740310 hasConceptScore W798740310C8639503 @default.
- W798740310 hasConceptScore W798740310C98045186 @default.
- W798740310 hasConceptScore W798740310C99498987 @default.
- W798740310 hasLocation W7987403101 @default.
- W798740310 hasOpenAccess W798740310 @default.
- W798740310 hasPrimaryLocation W7987403101 @default.
- W798740310 hasRelatedWork W154211054 @default.
- W798740310 hasRelatedWork W1976184852 @default.
- W798740310 hasRelatedWork W1990026624 @default.
- W798740310 hasRelatedWork W1998166704 @default.
- W798740310 hasRelatedWork W2067043745 @default.
- W798740310 hasRelatedWork W2118538769 @default.
- W798740310 hasRelatedWork W2142089368 @default.
- W798740310 hasRelatedWork W2165377795 @default.
- W798740310 hasRelatedWork W2167702096 @default.
- W798740310 hasRelatedWork W2247816280 @default.
- W798740310 hasRelatedWork W2599468100 @default.
- W798740310 hasRelatedWork W2780211329 @default.
- W798740310 hasRelatedWork W2798646778 @default.
- W798740310 hasRelatedWork W2963164771 @default.
- W798740310 hasRelatedWork W3028376820 @default.
- W798740310 hasRelatedWork W3129488543 @default.
- W798740310 hasRelatedWork W3172783726 @default.
- W798740310 hasRelatedWork W3189664570 @default.
- W798740310 hasRelatedWork W2740277077 @default.
- W798740310 hasRelatedWork W3144639247 @default.
- W798740310 isParatext "false" @default.
- W798740310 isRetracted "false" @default.
- W798740310 magId "798740310" @default.
- W798740310 workType "article" @default.