A Stochastic Configuration Network with Attenuation Regularization and Multi-kernel Learning and Its Application

To improve the generalization and robustness of the stochastic configuration network (SCN) model, an improved SCN algorithm is proposed. Attenuated L2 regularization is added to the iterative solution process of the output weight, which not only reduces the output weight of the hidden layer nodes in the early iteration, but also ensures that the new nodes after multiple iterations are not affected by the larger regularization coefficient, forming the constraint effect of first tight and then loose. In addition, the multi-kernel learning method is introduced to obtain the characteristics of the sample data, and the penalty weight matrix of the training samples is determined, so as to re-evaluate the output weight of the SCN model to enhance the robustness of the model. The performance of the proposed method was tested on four standard datasets and historical data from municipal solid waste incineration processes. The experimental results show that the modeling method proposed in this paper has certain advantages in improving the model’s generalization and robustness.