ReLU-FCM trained by quasi-oppositional bare bone imperialist competition algorithm for predicting employment rate

Fuzzy cognitive maps (FCMs) are a powerful tool for system modeling, which can be used for static and dynamic analysis. However, traditional FCMs are usually learned by gradient-based methods, and the adopted sigmoid nonlinear activation function frequently causes gradient saturation. These two shortcomings set a limit on the modeling accuracy. To overcome those problems, we propose in this paper a new FCM with two improvements. First, the rectified linear unit (ReLu) activation function is adopted to replace the sigmoid function. Second, a newly proposed quasi-oppositional bare bone imperialist competition algorithm (QBBICA) is used to learn the FCM. The improved FCM is used to predict the employment rate of graduates from Liren College, Yanshan University. Experimental results show that the improved FCM is effective in employment rate prediction.