A System Identification Method Based on Multi-layer Perception and Model Extraction
Artificial Neural Networks (ANNs) have provided an interesting and labor-saving approach for system identification. However, ANNs fall short when an explicit model is needed. In this paper, a method of getting the explicit model by extracting it from a trained ANN is proposed. To identify a system, a Multi-Layer Perceptron (MLP) is constructed, trained and a polynomial model is extracted from the trained network. This method is tested in the experiments and shows its capability for system identification, compared with the Least Squared method.
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