Evaluation of Innovation and Entrepreneurship Ability of Computer Majors Based on Neural Network Optimized by Particle Swarm Optimization
The current evaluation index systems (EISs) of innovation and entrepreneurship (I&E) ability are not sufficiently systematic, scientific, or practical. To solve the problem, this paper tries to evaluate the I&E ability of computer majors, using neural networks improved by particle swarm opti...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Kassel University Press
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ec8a7e4f73ec407e81fe5d70a468eeda |
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| Sumario: | The current evaluation index systems (EISs) of innovation and entrepreneurship (I&E) ability are not sufficiently systematic, scientific, or practical. To solve the problem, this paper tries to evaluate the I&E ability of computer majors, using neural networks improved by particle swarm optimization (PSO). Firstly, an EIS of 22 second-level indexes under 5 first-level indexes was designed to evaluate the I&E ability of college computer majors. Next, an evaluation model was developed based on fuzzy neural network (FNN), and the corresponding training algorithm was created. Moreover, an improved PSO was introduced to optimize the FNN, and the optimization process was detailed. The proposed model was proved effective through experiments. |
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