Chemical Laboratory Safety Evaluation Based on Discrete Hopfield Neural Network

To solve the issue of evaluating the safety risk of chemical laboratories in universities, a safety evaluation method based on discrete Hopfield neural network (DHNN) was adopted. First, a multi-index safety status evaluation system was established by using analytic hierarchy process. Then, the fuzzy comprehensive appraisal was used to quantify the evaluation indexes and encode evaluation indicators. Finally, an optimization algorithm based on learning rate was adopted. This method was compared with traditional evaluation methods and the results show that this method is capable of achieving an accurate evaluation of the sample. After applying this method to real scenarios, simulation results demonstrate that the index system is reasonable and feasible, and the evaluation accuracy is high, which can provide a reference for practical safety risk evaluation.