NDLI: Estimation of unmeasured inputs using recurrent neural networks and the extended Kalman filter

Content Provider	IEEE Xplore Digital Library
Author	Habtom, R. Litz, L.
Copyright Year	1997
Description	Author affiliation: Inst. for Process Autom., Kaiserslautern Univ., Germany (Habtom, R.)
Abstract	A multiple-input-multiple-output dynamic system, whose inputs can not be measured online but data for the training of a neural network can be made available, is considered. A method of estimating those online immeasurable inputs is proposed based on a recurrent neural network model of the system and using the extended Kalman filter (EKF). An experimental stirred tank heater and a simulation model of a drying process are employed to demonstrate the effectiveness of the method. The two systems are modeled using recurrent neural networks and the estimation of an unmeasured valve position in the case of the stirred tank heater and an unmeasured moisture content for the drying process is carried out using the EKF algorithm. The experimental and the simulation results substantiate the practical effects of the proposed method for a class of dynamic systems.
Starting Page	2067
Ending Page	2071
File Size	473136
Page Count	5
File Format	PDF
ISBN	0780341228
DOI	10.1109/ICNN.1997.614221
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-06-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Neural networks State estimation Signal processing Mathematical model Multi-layer neural network Feedforward neural networks Predictive models Nonlinear control systems Parameter estimation
Content Type	Text
Resource Type	Article

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