- 期刊
- OpenAccess
Comprehensive Evaluation Model for Academic Quality of Food Journals Based on Rough Set and Neural Network
並列摘要
In order to evaluate food journals efficiently and reasonably, this study puts forward a comprehensive evaluation model for academic quality of food journals based on rough set and neural network. Firstly, we reduce evaluation indicators of journals based on discernibility matrix in rough set theory, removing the miscellaneous indicators and form the core evaluation indicator system, so as to have a more effective training for BP neural network. Then, we use methods defined in our study to generate enough training samples for the neural network modeling based on the core evaluation system. Lastly, with the help of BP neural network algorithm to rank journals, thereby we establish a comprehensive evaluation model for academic quality of journal. Instance analysis of food journals shows that the principle of generating the sample is feasible and effective and the modeling process is reliable and reasonable. What’ more, the model established can be used for comprehensive evaluation for academic quality of food journals.