A self-tuning predictor

Wittenmark, Björn

doi:10.1109/tac.1974.1100734

Cited by 80 publications

(19 citation statements)

References 4 publications

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“…The reader can find further details about the algorithm in studies published by Kalman (1960), Wittenmark (1974), Ganendra (1976), Ambrus & Szollôsi-Nagy (1981), Young (1986) etc.…”

Section: Appendix: Recursive Formulae Of Self-tuning Predictormentioning

confidence: 99%

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Rainfall input in an adaptive river flow forecast model

Iritz

1992

Hydrological Sciences Journal

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This paper deals with adaptive flow forecasting from discharge observations, with catchment rainfall as an additional input component. The precipitation input is pre-processed by approximate expressions of excess rainfall and routed by a linear cascade model. The basic equations of the algorithm and the general pattern of the computer program are presented. Examples are given for a case study area (Vâsterdalâlven River in Sweden) where rainfall input in addition to the flow observations was used to improve the forecast accuracy. The results are discussed. Les précipitations comme données d'entrée dans un modèle adaptatif de prévision des débits fluviauxRésumé Cet article traite des débits fluviaux adaptatifs prévisionnels à partir des observations de débit, avec les précipitations sur le bassin versant intermédiaire comme données d'entrée additionnelles. Les données de précipitations sont prétraitées à l'aide d'expressions approximatives de pluie excédentaire et acheminées par un modèle en cascade linéaire. Les équations fondamentales de l'algorithme et la structure du programme sont présentées. Dans l'article sont présentées des exemples de la région étudié (la rivière de Vâsterdalâlven en Suède) où les données des précipitations ont été introduites dans le modèle pour compléter les observations de débit afin d'obtenir des prévisions plus exactes. Les résultats ainsi obtenus sont discutés.

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“…The reader can find further details about the algorithm in studies published by Kalman (1960), Wittenmark (1974), Ganendra (1976), Ambrus & Szollôsi-Nagy (1981), Young (1986) etc.…”

Section: Appendix: Recursive Formulae Of Self-tuning Predictormentioning

confidence: 99%

“…2). A detailed theoretical description of the self-tuning predictor is given by Wittenmark (1974). The recursive steps of the self-tuning algorithm are also presented by Ganendra (1976), Ambrus (1980) and Iritz (1988).…”

Section: Introductionmentioning

confidence: 99%

Rainfall input in an adaptive river flow forecast model

Iritz

1992

Hydrological Sciences Journal

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“…The self-tuning predictor was developed by Wittenmark (1974). Hydrological applications of the self-tuning predictor are reported by Ganendra (1976) and Ambrus (1980).…”

Section: Self-tuning Predictor (Setu)mentioning

confidence: 99%

River flow forecasting system with data management, graphical river network control and interactive operation

Iritz

1990

Hydrological Sciences Journal

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The feasibility and success of a forecasting system depend on many factors. Although the models may be of the highest accuracy, they cannot make a forecasting system effective without a well organized database and interprétable output. The interaction between the processes of data management, computation and output presentation should be dynamic. A complete river forecasting system for IBM (or compatible) personal computers aimed at meeting the above demands has been developed at the Department of Hydrology of Uppsala University. The program package sets up the river network, stores a database for it, supplies the appropriate data for a wide choice of forecasting models, and gives interprétable output. The functions are supported by screen graphics. The paper presents this system, the most important points of the algorithms, and an example application.Système de prévision des écoulements fluviaux, gesfioa des données, contrôle graphique du réseau des rivières avec opérations interactives

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“…+ ~21v2(t) (22) where Vl(t ) = Yw(t) and v2(t ) = ~d(t) are respectively synthetic, deterministic, TI~m (hrs) Figure 5 The results of the one-step ahead adaptive predictor Simultaneously, the estimate of eo is considerably adapted in order to track the changing properties of the system's dynamics, of which the structure of the four-step ahead predictor is relatively "ignorant". However after such temporary disturbances returns slowly to its steady-state value for dry-weather conditions thus giving o a good illustration of the adaptability of the predictor (see figure 7).…”

Section: ~(T+llt) = %Y(t) -%9(tlt-1) +~Ovl(t+l) +~Lvl(t) +Y2ov2(t+l)mentioning

confidence: 99%

The identification and adaptive prediction of urban sewer flows

Beck¹

1976

Optimization Techniques Modeling and Optimization in the Service of Man Part 1

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A self-tuning predictor

Cited by 80 publications

References 4 publications

Rainfall input in an adaptive river flow forecast model

Rainfall input in an adaptive river flow forecast model

River flow forecasting system with data management, graphical river network control and interactive operation

The identification and adaptive prediction of urban sewer flows

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