A Training Algorithm for Locally Recurrent NN Based on Explicit Gradient of Error in Fault Detection Problems
In this work a diagnostic approach for nonlinear systems is presented. The diagnosis is performed resorting to a neural predictor of the output of the system, and by using the error prediction as a feature for the diagnosis. A locally recurrent neural network is used as predictor, after it has been trained on a reference behavior of the system. In order to model the system under test a novel training algorithm that uses an explicit calculation of the cost function gradient is proposed. The residuals of the prediction are affected by the deviation of the parameters from their nominal values. In this way, by a simple statistical analysis of the residuals, we can perform a diagnosis of the system. The Rössler hyperchaotic system is used as benchmark problem in order to validate the diagnostic neural approach proposed.
KeywordsLocally recurrent neural networks nonlinear systems diagnosis gradient-based training
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