Abstract
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.
Publisher
Public Library of Science (PLoS)
Reference41 articles.
1. Intelligent employment rate prediction model based on a neural computing framework and human-computer interaction platform;T Wang;Neural Computing and Applications,2020
2. Guo T, Xia F, Zhen S, Bai X, Zhang D, Liu Z, et al. Graduate Employment Prediction with Bias. In: Proceedings of the 34-th AAAI Conference on Artificial Intelligence (AAAI),. vol. 34; 2020. p. 670–677.
3. Research on application of BP neural network in predicting employment rate of college graduates;D Ma;Journaal of Jiamusi University (Natural Science Edition),2014
4. College students employment forecasting model based on IAFSA-BP parallel integrated learning algorithm;Z Jiang;Value Engineering,2019
5. Wang Y. Research on University Students’ employmnet Prediction model and Application based on Decision tree algorithm. Central China Normal University; 2018.