电子测量与仪器学报

乔景慧,张岩,陈宇曦,张开济.遗忘因子随机配置网络驱动的自适应切换学习模型[J].电子测量与仪器学报,2023,37(8):71-83

遗忘因子随机配置网络驱动的自适应切换学习模型

Adaptive switching learning model based on forgetting factor stochastic configuration networks

DOI：

英文关键词:stochastic configuration networks forgetting factor dynamic hidden nodes adaptive switching learning model

基金项目:国家自然科学基金(61573249)、辽宁省自然科学基金 (2019-MS-246) 、辽宁省教育厅基金(LZGD2019002)、辽宁省高等学校创新人才项目(LR2019048)、沈阳工业大学重点科研基金(ZDZRGD2020004)、辽宁省研究生教育教学改革研究项目(LNYJG2022073)、沈阳工业大学研究生教育教学改革研究项目(SYJG20222002)资助

作者	单位
乔景慧	1.沈阳工业大学机械工程学院
张岩	1.沈阳工业大学机械工程学院
陈宇曦	1.沈阳工业大学机械工程学院
张开济	1.沈阳工业大学机械工程学院

Author	Institution
Qiao Jinghui	1.School of Mechanical Engineering, Shenyang University of Technology
Zhang Yan	1.School of Mechanical Engineering, Shenyang University of Technology
Chen Yuxi	1.School of Mechanical Engineering, Shenyang University of Technology
Zhang Kaiji	1.School of Mechanical Engineering, Shenyang University of Technology

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中文摘要:

随机配置网络(SCNs)具有通用逼近能力和快速建模特性,已成功应用于大数据分析。在 SCN 的基础上,块增量随机配置网络( BSC)使用块增量机制提高训练速度,但增加了模型结构的复杂程度。为了解决上述难题,提出遗忘因子随机配置网络(FSCN-I 和 FSCN-II)驱动的自适应切换学习模型(ASLM)。该模型利用正态分布配置隐含层节点的输入参数。 FSCN-I 通过误差值和遗忘因子调整节点块的尺寸,提高训练速度。 FSCN-II 引入节点移除机制降低模型结构的复杂程度。 ASLM 由 FSCN-I 和 FSCN-II 构成,两者根据自适应变化的边界随机切换以提高模型的训练速度,并在 FSCN-I 的基础上降低模型结构的复杂程度。最后,通过基础数据集和工业实例,表明该方法的有效性。

英文摘要:

Stochastic configuration networks (SCNs) have been successfully applied to big data analysis with their general approximation capability and fast modeling properties. Based on the SCNs, stochastic configuration networks with block increments (BSC) use block increment mechanism to improve the training speed, but increase the complexity of model structure. To solve the above challenges, an adaptive switching learning model based on forgetting factor stochastic configuration networks (FSCN-I and FSCN-II) with (ASLM) is proposed. FSCN-I adjusts the size of node blocks by error values and forgetting factors to improve the training speed, and FSCN-II introduces a node removal mechanism to reduce the complexity of the model structure. ASLM consists of FSCN-I and FSCN-II, both of which are randomly switched according to the adaptively changing boundaries to improve the training speed of the model and the complexity of the model structure is reduced based on FSCN-I. Finally, the effectiveness of the method is demonstrated with the underlying dataset and industrial examples.

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