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Ant Colony Algorithm for Evaluation of University Training Base Teaching

Yang Zhiwen, Hai Liu

Abstract


Base training education evaluation occupies an important position in university performance evaluation and becomes a hotspot research field for related researchers. The paper presents a new evaluation algorithm for university training base teaching based on improved BP neural network. Firstly an evaluation indicator system of university training base teaching evaluation is designed through analyzing the aspects of teaching effects and teacher factors and student factors. Secondly, BP neural network and BP neural network are integrated to overcome its shortages of low convergence in calculation based on analysis of BP working principle and defects reasons. Thirdly data from three universities are taken for examples to verify the validity and feasibility of the model and the experimental results show that the model can evaluate university base teaching practically and can help university and university teachers take corresponding concrete measures to enhance its education performance.


Keywords


Ant colony algorithm, BP neural network, performance evaluation, university training base teaching evaluation.

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