Incorporating Recurrent Networks for Online System Identification Alongside Traditional Sine-Sweep Experiments

The experimental identification of an unknown system, and the blind system identification (BSI) methods, allows engineers to establish mathematical models that represent the real system behavior. However, when the system operates in a non-stationary environments influenced by external disturbances,...

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Bibliographic Details
Published in:	IEEE access Vol. 12; pp. 56033 - 56041
Main Author:	Segura, C. A. L.
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation models Adaptive models Adaptive systems artificial neural networks Convexity Cost function Disturbances Estimation Identification methods Information theory Iterative methods Learning theory Mathematical models Nonstationary environments On-line systems online learning recurrent neural network (RNN) System identification time-lagged recurrent network (TLRN) Transfer functions
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Summary:	The experimental identification of an unknown system, and the blind system identification (BSI) methods, allows engineers to establish mathematical models that represent the real system behavior. However, when the system operates in a non-stationary environments influenced by external disturbances, models with adaptive properties are required for predicting the real-time domain response. This study defines and analyzes in detail two system identification methods. The first method, which operates offline and requires post-processing, is mathematically defined to achieve the highest level of automation. It is based on sine sweep theory and involves conducting long-term experiments on a real system to determine its frequency domain properties. The second method, which operates online, employs computational learning theory and information theory to predict the system response through online learning. This modern approach uses convex optimization to obtain the optimal parameters of a time-lagged recurrent network (TLRN) in each iteration, which incorporates, among other features, a gamma filter as a mapper. This iterative online method was mathematically described addressing stability, convergence, and disturbances issues.
ISSN:	2169-3536 2169-3536
DOI:	10.1109/ACCESS.2024.3385236