Evaluation model and algorithm optimization of intelligent manufacturing system on the basis of BP neural network

Abstract

Intelligent manufacturing is a key direction for the development of the manufacturing industry. Since the level of intelligent manufacturing varies among enterprises, building a scientific and reliable evaluation model is the key to evaluate enterprises. The study optimizes the Back Propagation (BP) neural network based on the Firefly Algorithm (FA) and innovatively introduces the Sparrow Search Algorithm (SSA), which changes the search mechanism of the BP and improves the traditional BP affected by the local optimum. of the traditional BP. In the experimental results, the average error of the improved model is 1.34 % and 0.41 % under the optimal preset parameters, which is significantly better than other advanced algorithms. It is verified that the improved evaluation model in this study has lower error and better effect, which has better application value in the evaluation of intelligent manufacturing system.