Multi-Target Stochastic Configuration Network Model Based on Collaborative Optimization and Its Applications

A multi-target model can simultaneously monitor the changing trends of multiple related parameters, which plays a crucial role in achieving intelligent perception and decision-making in the production process. However, efficiently learning the non-linearity between input and output variables and exploring the correlations between multiple targets to construct high-performance monitoring models have always been challenging. This paper proposes a multi-target stochastic configuration network (SCN) model from the perspectives of network structure design and parameter group sparse constraints. This model assigns hidden layer input weights and biases using a weighted supervisory mechanism and adds a latent layer to the network structure. It facilitates multi-target balanced learning while exploiting inter-target correlations to improve modeling accuracy. The output weights of the hidden and latent layers are collaboratively constrained by constructing a generalized sparse matrix elastic net to induce their group sparsification. An alternating optimization algorithm based on matrix decomposition is designed to iteratively solve the model parameters. The performance of the proposed model is experimentally validated on benchmark multi-target datasets and historical data of municipal solid waste incineration process. The results demonstrate that the proposed model achieves superior accuracy and exhibits potential for multi-target modeling applications.