Wang Yang, Zhou Tao, Yang Guanci, Zhang Chenglong, Li Shaobo
State Key Laboratory of Public Big Data, Guizhou University, Guiyang, Guizhou, China.
Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, Guizhou, China.
PeerJ Comput Sci. 2023 May 30;9:e1382. doi: 10.7717/peerj-cs.1382. eCollection 2023.
The stochastic configuration network (SCN) randomly configures the input weights and biases of hidden layers under a set of inequality constraints to guarantee its universal approximation property. The SCN has demonstrated great potential for fast and efficient data modeling. However, the prediction accuracy and convergence rate of SCN are frequently impacted by the parameter settings of the model. The weighted mean of vectors (INFO) is an innovative swarm intelligence optimization algorithm, with an optimization procedure consisting of three phases: updating rule, vector combining, and a local search. This article aimed at establishing a new regularized SCN based on the weighted mean of vectors (RSCN-INFO) to optimize its parameter selection and network structure. The regularization term that combines the ridge method with the residual error feedback was introduced into the objective function in order to dynamically adjust the training parameters. Meanwhile, INFO was employed to automatically explore an appropriate four-dimensional parameter vector for RSCN. The selected parameters may lead to a compact network architecture with a faster reduction of the network residual error. Simulation results over some benchmark datasets demonstrated that the proposed RSCN-INFO showed superior performance with respect to parameter setting, fast convergence, and network compactness compared with other contrast algorithms.
随机配置网络（SCN）在一组不等式约束下随机配置隐藏层的输入权重和偏差，以保证其通用逼近特性。SCN已在快速高效的数据建模方面展现出巨大潜力。然而，SCN的预测精度和收敛速度经常受到模型参数设置的影响。向量加权均值（INFO）是一种创新的群体智能优化算法，其优化过程包括三个阶段：更新规则、向量组合和局部搜索。本文旨在基于向量加权均值建立一种新的正则化SCN（RSCN-INFO），以优化其参数选择和网络结构。将结合岭方法和残差反馈的正则化项引入目标函数，以便动态调整训练参数。同时，采用INFO自动为RSCN探索合适的四维参数向量。所选参数可能会导致网络架构紧凑，网络残差误差更快减小。在一些基准数据集上的仿真结果表明，与其他对比算法相比，所提出的RSCN-INFO在参数设置、快速收敛和网络紧凑性方面表现出卓越性能。
