Neural Network-Based Approach for Evaluating College English Teaching Methodology

A fair and scientific method of evaluating collegiate English instruction is currently lacking. Traditional methods are commonly used to evaluate the quality college education in terms standard statistical analysis evaluation model. As teaching a nonlinear issue, above strategies have achieved some good results but certain limitations. They not objective selection indicators, or setting index weights, there degree subjectivity. Artificial neural network (ANN) widely massive fields due its characteristics processing, adaptive learning, high fault tolerance. kind network, BP has strong mapping ability, so it feasible solve relationship (ETE). Therefore, this work, we first designed an ETE system index. Then, strategy with designed, which can carry out high-performance modeling for In order alleviate issue slow convergence speed fall into local optimum, work also combines particle swarm algorithm further improve performance. Massive experiments proved reliability effectiveness work.