基于模糊神经网络的员工晋升辅助决策系统

Computer Aided Decision-Making System for Staff Promotion Based on Fuzzy Neural Network

为定量地分析企业员工晋升决策过程的影响因素,用基于实例的模糊神经网络方法,通过划分非模糊化和模糊化输入变量,改进了Takagi-Sugeno模糊模型.用包含求和、求积和求极小运算节点的扩展的p i-sigm a神经网络学习算法确定结论参数和前提参数,自适应地从员工数据库中归纳出模糊逻辑关系,建立企业晋升决策的辅助系统.经算例验证准确率达75%以上.

To analyze the factors influencing staff promotion, the Takagi-Sugen model, a method of fuzzy neural network, was modified. The input variables in the modified model include fuzzy and nonfuzzy ones. An extended pi-sigma artificial neural network algorithm, in which summation, multiplication and minimization nodes are available, was used to determine conclusion and premise parameters, and to summarize the fuzzy logic from staff databases adaptively. An example shows that the accurate rate of the method is over 75%.