In order to reduce the evaluation error caused by the random selection of initial weights and thresholds in the BP neural network,a mathematics discipline quality evaluation model is constructed by integrating the QPSO algorithm in the BP neural network.Based on the principal component analysis,key indicator components are extracted from 19 secondary indicators for quality evaluation,and the contribution rate of secondary indicators is calculated.After data dimensionality reduction,indicators with a cumulative contribution rate of no less than 85％are selected and input into the BP neural network model.The QPSO algorithm is used to optimize the initial weights and thresholds of the BP neural network,update the particle position,the local optimal position and global optimal position of the current particle are considered,and the"particle average optimal position"is introduced to strengthen the interaction between particles,and the weight coefficient was used to balance the particle convergence ability.Therefore,the QPSO-BP mathematics subject quality evaluation model is constructed,and the effectiveness of mathematical discipline quality evaluation can be divided into four levels:excellent,good,moderate,and poor.The experimental results show that the mathematical subject quality evaluation model integrating QPSO algorithm can retain indicators with a cumulative contribution rate of 85％,and the evaluation errors are all lower than the preset error of 0.01.The model has good convergence performance and the quality evaluation results of the mathematical discipline are in line with the actual situation,avoiding subjective randomness and providing effective quality feedback for the construction of the mathematical discipline.
维普
万方数据
