A Multitarget Online Fuzzy Stochastic Configuration Network for Industry Data Modeling

To address the challenge of multitarget parameters and non-stationary data streams in industrial data modeling, this paper proposes a multitarget online fuzzy stochastic configuration network (MOF-SCN) modeling method. The method consists of two parts: offline modeling and online optimization. The offline phase employs a multitarget sparse regularization strategy to enhance the learning mechanism of the fuzzy stochastic configuration network (F-SCN), improving the model's capacity to handle multitarget parameters. The online phase proposes two strategies, namely, a fuzzy rule growth and pruning strategy and an online optimization strategy for the enhancement layer of F-SCN, aimed at achieving real-time dynamic optimization of the model structure and parameters. This enhances the model's continual learning ability in non-stationary online data streams. Comparative experiments on two real-world industrial datasets demonstrate that the proposed method significantly outperforms in terms of adaptability and accuracy, thereby extending the applicability of F-SCN.