Modeling job performance using Optimized Adaptive Neuro-Fuzzy Inference System
Using current employee performance data to predict the future behavior of the applicants is an interesting area which can broaden new horizons of knowledge lay in the organization. Because of inherent ambiguity and uncertainty, cognitive limitations of the human mind make unknown behaviors of very c...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
| Published: |
University of Tehran
2014-03-01
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| Series: | مدیریت صنعتی |
| Subjects: | |
| Online Access: | https://imj.ut.ac.ir/article_52238_1195e14876343c921e8b58f7fdc2baf2.pdf |
| Summary: | Using current employee performance data to predict the future behavior of the applicants is an interesting area which can broaden new horizons of knowledge lay in the organization. Because of inherent ambiguity and uncertainty, cognitive limitations of the human mind make unknown behaviors of very complex systems difficult to predict. As a consequence, it is necessary to model the imprecise modes of reasoning to make rational decisions in an environment of uncertainty and imprecision. In this paper, artificial intelligence and advanced algorithms is introduced as an Adaptive Neuro-Fuzzy Inference Optimized System in order to model the job performance. The correlation coefficient is 0.9956 which indicates high accuracy of extracted model, minimum error and maximum adaptability to predict job performance with actual performance. This approach provides an effective tool for managers in order to avoid subjective judgment errors inherent in human decision making. |
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| ISSN: | 2008-5885 2423-5369 |