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...

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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
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Abstract	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.
AbstractList	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.
Author	Wu, Shang Wang, Qianjin Dai, Wei Hong, Qiqiang
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Cites_doi	10.1016/j.ins.2019.04.055 10.1016/j.ins.2019.01.062 10.1016/j.ins.2017.06.017 10.1109/TCYB.2017.2734043 10.1016/j.patrec.2021.04.025 10.1016/j.ins.2019.02.066 10.1016/j.neucom.2020.02.029 10.1016/j.knosys.2018.06.032 10.1109/TNNLS.2017.2651068 10.1007/978-3-319-24282-8_9 10.1016/0925-2312(94)90053-1 10.1016/j.ins.2020.05.112 10.1109/TNNLS.2021.3053306 10.1016/j.scitotenv.2020.136991 10.1109/TNNLS.2018.2866622 10.1109/TNSRE.2017.2711264 10.1007/s10994-016-5546-z 10.1109/TII.2019.2954351 10.16383/j.aas.c190602 10.1109/TAFFC.2020.3043135 10.1109/LGRS.2011.2109934 10.1016/j.media.2015.07.003 10.1109/TII.2021.3096840 10.1109/TNNLS.2017.2716952 10.1016/j.ins.2016.12.007 10.1109/TCYB.2021.3061094 10.1109/TNNLS.2020.2967816 10.1109/TII.2019.2892873 10.1109/TCYB.2018.2857815 10.1109/TPAMI.2017.2688363 10.1016/j.ins.2017.07.003
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References	ref13 ref12 ref15 ref14 ref31 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref16 ref19 ref18 Nie (ref30) ref24 ref23 ref26 ref25 Ando (ref4) 2005; 6 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref3 ref6 ref5
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Snippet	Existing stochastic configuration network (SCN)-based modeling methods are underperformed in handling multitarget regression problems. An important reason is...
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SubjectTerms	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
Title	Multitarget Stochastic Configuration Network and Applications
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