Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162758
Title: Experimental evaluation of stochastic configuration networks: is SC algorithm inferior to hyper-parameter optimization method?
Authors: Hu, Minghui
Suganthan, Ponnuthurai Nagaratnam
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Source: Hu, M. & Suganthan, P. N. (2022). Experimental evaluation of stochastic configuration networks: is SC algorithm inferior to hyper-parameter optimization method?. Applied Soft Computing, 126, 109257-. https://dx.doi.org/10.1016/j.asoc.2022.109257
Journal: Applied Soft Computing
Abstract: To overcome the pitfalls of Random Vector Functional Link (RVFL), a network called Stochastic Configuration Networks (SCN) has been proposed. By constraining and adaptively selecting the range of randomized parameters using the Stochastic Configuration (SC) algorithm, SCN claims to be potent in building an incremental randomized learning system according to residual error minimization. The SC has three variants depending on how the range of output weights are updated. In this work, we first relate the SCN to appropriate literature. Subsequently, we show that the major parts of the SC algorithm can be replaced by a generic hyper-parameter optimization method to obtain overall better results.
URI: https://hdl.handle.net/10356/162758
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2022.109257
Rights: © 2022 Elsevier B.V. All rights reserved. This paper was published in Applied Soft Computing and is made available with permission of Elsevier B.V.
Fulltext Permission: embargo_20241007
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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