Multitarget Stochastic Configuration Network and Applications

Existing stochastic configuration network (SCN)-based modeling methods are underperformed in handling multitarget regression problems. An important reason is that they ignore the intertarget correlations, which have an important effect on improving the modeling accuracy. To enhance the performance o...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on artificial intelligence Jg. 4; H. 2; S. 338 - 348
Hauptverfasser: Wang, Qianjin, Hong, Qiqiang, Wu, Shang, Dai, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2023
Schlagworte:
Adaptation models
Alternating optimization algorithm
Correlation
Data models
multitarget regression
Predictive models
stochastic configuration network (SCN)
Stochastic processes
structure matrix
supervisory mechanism
Task analysis
Training
ISSN:2691-4581, 2691-4581
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Existing stochastic configuration network (SCN)-based modeling methods are underperformed in handling multitarget regression problems. An important reason is that they ignore the intertarget correlations, which have an important effect on improving the modeling accuracy. To enhance the performance of these SCN-based models, in this article, a novel multitarget SCN (MTSCN) modeling approach is presented. The L 2,1 norm of a structure matrix can be utilized to explicitly reveal the correlations between multiple targets and an L 2 term are attached to the cost function of SCN. Considering the nonsmoothness of the constructed cost function, an alternating optimization algorithm is adopted to compute the structure matrix and the output weights of MTSCN. Then, a new supervisory mechanism is proposed to ensure the convergence of MTSCN. Finally, experimental results using the synthetic data and several real-world datasets show that the developed MTSCN is more superior to other modeling methods in resolving multitarget modeling problems.
ISSN:2691-4581
2691-4581
DOI:10.1109/TAI.2022.3162570