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Neural Processing Letters

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24-09-2022

An Outlier-Robust Growing Local Model Network for Recursive System Identification

Authors: Jéssyca A. Bessa, Guilherme A. Barreto, Ajalmar R. Rocha-Neto

Published in: Neural Processing Letters | Issue 4/2023

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Abstract

In this paper, we develop a self-growing variant of the local model network (LMN) for recursive dynamical system identification. The proposed model has the following features: growing online structure, fast recursive updating rules, better memory use (no storage of covariance matrices is required), and outlier-robustness. In this regard, efficiency in performance and simplicity of implementation are the essential qualities of the proposed approach. The proposed growing version of the LMN model results from a synergistic amalgamation of two simple but powerful ideas. For this purpose, we adapt the neuron insertion strategy of the resource-allocating network to LMN model, and replaces the standard OLS rule for parameter estimation with outlier-robust recursive rules. A comprehensive evaluation involving three SISO and one MIMO benchmarking data sets corroborates the proposed approach’s superior predictive performance in outlier-contaminated scenarios compared to fixed-size LMN-based models.

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Title: An Outlier-Robust Growing Local Model Network for Recursive System Identification
Authors: Jéssyca A. Bessa
Guilherme A. Barreto
Ajalmar R. Rocha-Neto
Publication date: 24-09-2022
Publisher: Springer US
Published in: Neural Processing Letters / Issue 4/2023
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-022-11040-z

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