Model Construction of Higher Education Quality Assurance System Based on Fuzzy Neural Network (Informatica, 2024)
This paper uses the principles and advantages such as fuzzy law and neural network, integrates them organically, and designs a model of higher education quality assurance system based on the fuzzy neural network using MATLAB software. For the problems that the law quantifies the fuzzy information and does not have adaptive learning ability, this paper proposes a heterogeneous wireless network access selection algorithm based on DA-FNN. The algorithm combines fuzzy logic theory and neural network, which enables the algorithm to process fuzzy network attribute information while adaptively adjusting parameters, and uses the dragonfly algorithm to optimize the parameters of the affiliation function of the fuzzy neural network to make the network evaluation results more accurate and thus improve the system performance. To realize the separation of front and back ends and effectively improve the user experience, the MVVM (Model-View-View Model) model is used for the design. The system development process is standardized to facilitate system management with requirements analysis, conceptual design, detailed design, coding, and testing. The system is validated in the simulation software MATLAB, and the functions of sample maintenance, fuzzy neural network training, and fuzzy neural network evaluation are implemented in the student evaluation subsystem as an example. The fuzzy evaluation algorithm of higher education quality based on fuzzy neural network architecture proposed in this paper can effectively integrate the positive propagation mechanism, multi-level feedback mechanism, and fuzzy quality evaluation mechanism in the talent training system and provide a theoretical basis for rational analysis of the weak points of student ability development in the talent training process.