Research Progress on Stochastic Configuration Networks

Research Progress on Stochastic Configuration Networks

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Research Progress on Stochastic Configuration Networks

Zhang Chenglong, Ding Shifei, Guo Lili, Zhang Jian

Journal of Software

Journal of Software

Abstract

The stochastic configuration network (SCN) is an emerging incremental neural network model. Unlike other randomized neural network methods, it can configure hidden layer node parameters through a supervisory mechanism, ensuring the model’s fast convergence performance. Due to its advantages such as high learning efficiency, low human intervention, and strong generalization ability, SCN has attracted significant research interest from scholars both domestically and internationally since its introduction in 2017, leading to rapid promotion and development. This article comprehensively summarizes the research progress of SCN from various aspects including its basic theory, typical algorithm variants, application areas, and future research directions. Firstly, it analyzes the algorithm principles, universal approximation performance, and advantages of SCN from a theoretical perspective; secondly, it focuses on typical variants such as deep SCN, two-dimensional SCN, robust SCN, ensemble SCN, distributed parallel SCN, and regularized SCN; subsequently, it introduces the application progress of SCN in different fields such as hardware implementation, computer vision, medical data analysis, fault detection and diagnosis, and system modeling and prediction; finally, it points out the development potential of SCN in research directions such as convolutional neural network architecture, semi-supervised learning, unsupervised learning, multi-view learning, fuzzy neural networks, and recurrent neural networks.

Research Progress on Stochastic Configuration NetworksKeywords: stochastic configuration networks; neural networks; deep learning; randomized learning; research progressResearch Progress on Stochastic Configuration Networks

Excerpt from Excellent Chapters

Research Progress on Stochastic Configuration Networks

Research Progress on Stochastic Configuration Networks

Research Progress on Stochastic Configuration Networks

Research Progress on Stochastic Configuration Networks

Research Progress on Stochastic Configuration Networks

Research Progress on Stochastic Configuration Networks

Research Progress on Stochastic Configuration Networks

Research Progress on Stochastic Configuration Networks

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Research Progress on Stochastic Configuration Networks

Zhang Chenglong, Ding Shifei, Guo Lili, Zhang Jian

Journal of Software, 2024, 35(5): 2379-2399Please copy the link or scan the QR code below to read the original texthttp://www.jos.org.cn/jos/article/abstract/6804Research Progress on Stochastic Configuration NetworksCopyright Statement

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Research Progress on Stochastic Configuration Networks

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