Affiliations: [a] Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China
| [b] Jiangsu Engineering Center of Network Monitoring and School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
Correspondence:
[*]
Corresponding author. Xiaoyong Liu, Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China. E-mail: [email protected].
Abstract: Reasonable structure of human resource is of great significance to development of an organization, so accurate prediction of human resource structure is a very important research problem. Adaptive Neuro-Fuzzy Inference System (abbreviated as ANFIS) is a high-efficiency learning model, and its distributed network structure has very effective result in establishing nonlinear model and constructing time series prediction model. However, classical ANFIS has some disadvantages, such as difficult determination of structure and large randomness of training parameter setting. This paper provides a hybrid prediction model of human resource structure by using the algorithm based on fusion of PSO with random weight, RPSO, and ANFIS, named RPSO-ANFIS. The novel algorithm uses RPSO to train relevant parameters of ANFIS and determine network structure of ANFIS. Empirical results shows that, compared with GA-ANFIS and PSO-ANFIS, RPSO-ANFIS has advantages of rapid learning speed, high prediction accuracy and smaller relative mean error, which indicated RPSO-ANFIS has good practical application value in predicting the structure of human resource.
Keywords: Adaptive neuro-fuzzy inference system, PSO, the structure of human resources, random weight, fuzzy logic
