Multitarget Incremental Modeling via Random Weight Networks

Incremental random weight networks (IRWNs) are increasingly being employed for modeling industrial processes. However, since most of the existing IRWNs do not consider the relationships between multiple targets, they have limited capability of conducting the task of multitarget modeling encountered in many industrial processes. In this article, a novel multitarget IRWN integrating
$L_{2,1}$
-norm regularization, termed as MTIRWN, is proposed to learn potential intertarget correlations for improved modeling performance. First, the feature layer is inserted between the input layer and hidden layer, based on this, the input data are fed into a more proper feature space using the feature mapping approach. Then, to benefit from the relationships among targets, an objective function using
$L_{2,1}$
-norm regularization is used, and a new inequality constraint that can explore the influence relationships between the newly added hidden node and generalization performance is constructed by the Greville's method. Then, alternating direction method of multiplier (ADMM) is employed to optimize the output weights. Finally, an extensive evaluation is untaken on six real-word datasets and a real multitarget modeling problem, which indicates that the proposed algorithm can effectively solve the multitarget modeling problems.