Stochastic configuration network modeling method based on information superposition and mixture correntropy

To improve the generalizability and robustness of stochastic configuration networks (SCNs), this paper proposes a robust modeling method based on information superposition and mixture correntropy. First, the mapping information of the (sigmoid) activation function and its derivative function is superimposed, and the hidden layer parameters are randomly assigned through a supervisory mechanism to improve the diversity of the hidden layer mapping. Second, mixture correntropy is used to construct a robust loss function, and different Gaussian kernels are used to measure the contribution of training samples to suppress the negative impact of data noise on the accuracy of the model. Finally, the performance of the proposed modeling method is tested on functional approximation, four benchmark datasets, and historical data from the municipal solid waste incineration process. The experimental results show that the modeling method proposed in this paper has advantages in terms of generalizability and robustness.