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The ill-posed problems of SCNs
In SCNs, the hidden layer parameters are assigned randomly under a supervisory mechanism, and the output weights are calculated using the least-squares method. The output weights can be analytically determined by finding the least-squares solution of the linear system H L β = T .
Jun 7, 2023
Jun 7, 2023 · The inequality constraint in stochastic configuration networks (SCNs) is key to their universal approximation capability.
This study examined the supervisory mechanism of SCNs and algebraic properties of the hidden output matrix, thereby proposing a new greedy stochastic ...
Request PDF | On Mar 1, 2023, Tao Zhou and others published Greedy stochastic configuration networks for ill-posed problems | Find, read and cite all the ...
GSCN. A new greedy stochastic configuration network, termed GSCN, with fast convergence rates for ill-posed problems.
Greedy stochastic configuration networks for ill-posed problems · Author Picture Tao Zhou. State Key Laboratory of Public Big Data, Guizhou University, Guiyang, ...
Zhou, Greedy stochastic configuration networks for ill-posed problems, Knowl.-Based Syst., № 269 https://doi.org/10.1016/j.knosys.2023.110464; Wu, Fast ...
The Block Jacobi and Gauss-Seidel methods are used to solve the output weights of ill-posed equations iteratively based on heterogeneous feature groups, in ...
This paper contributes to a development of randomized methods for neural networks. The proposed learner model is generated incrementally by stochastic.
Tytuł: Greedy stochastic configuration networks for ill-posed problems; Autorzy: Zhou, Tao · Wang, Yang · Yang, Guanci · Zhang, Chenglong