Stochastic configuration network (SCN) is a universal approximator which can be automatically and quickly constructed under the supervision mechanism with inequality constraint. It has potential advantages in the field of large data modeling. In order to enhance the accuracy and stability of the model prediction, a stochastic configuration network model with L2 norm regularization (L2-SCN) based on the classical SCN is proposed to improve the algebraic properties of output weighted least squares analytical solutions and avoid the structural risk of the model overfitting. For the ball mill load operation status recognition under a wide range of non-stationary operating conditions, L2-SCN method was used to identify the ball mill load operating conditions. The experiment results on the ball mill show that the proposed L2-SCN model has relative advantages in terms of accuracy and stability compared with the classic SCN model and the random vector functional link network (RVFL).

To ensure virtual enterprise ( VE) reaches the desired goals，the owner should design effective mechanisms to avoid moral
hazard． In view of the VE exists principal agent relationship between the owner and the partner，we incorporate the partner’s fairness
preferences psychology and design an effective incentive mechanism considering the partner’s fairness concern to prevent the moral
hazard problem，and compared to the traditional principal agent model． Analysis results show that the fairness preference has changed
some of the conclusions of the traditional principal agent model that influences the effort level and the revenue sharing． The owner
should try to choose the partners with lower intensity fairness preferences psychology to avoid moral hazard in order to increase its own
profit．