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Training a neural observer using a hybrid approach | IEEE Conference Publication | IEEE Xplore

Training a neural observer using a hybrid approach


Abstract:

In this work, we use the approach based on observers such as the neural observer in order to introduce the diagnosis of nonlinear systems. There are different techniques ...Show More

Abstract:

In this work, we use the approach based on observers such as the neural observer in order to introduce the diagnosis of nonlinear systems. There are different techniques for training the neural networks. Among these techniques, we quote the backpropagation technique, the backpropagation technique with momentum and the hybrid one which is a mixture between the backpropagation technique and the sliding variable structure. The robustness of this kind of training for neural observer is tested through a physical example. The obtained results show that the third type of training is better than using a classic kind of training especially concerning the rapidity of convergence.
Date of Conference: 20-23 March 2012
Date Added to IEEE Xplore: 10 May 2012
ISBN Information:
Conference Location: Chemnitz, Germany

References

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