Improved nonlinear predictive control performance using recurrent neural networks | IEEE Conference Publication | IEEE Xplore

Improved nonlinear predictive control performance using recurrent neural networks


Abstract:

Recurrent neural networks are known to have better multi-step predictive capability compared to feedforward neural networks, with the disadvantage that they are more diff...Show More

Abstract:

Recurrent neural networks are known to have better multi-step predictive capability compared to feedforward neural networks, with the disadvantage that they are more difficult to train. This paper develops a novel recurrent neural network architecture, the structure of which allows formulation as a time varying linear model. Based on a quadruple tank challenge problem, the proposed recurrent neural network is shown to have superior performance compared to a similarly designed feedforward neural network.
Date of Conference: 11-13 June 2008
Date Added to IEEE Xplore: 05 August 2008
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Conference Location: Seattle, WA, USA

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