BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance

Abstract

Comprehensive performance evaluation is an important basis for improving the training effect of enterprise employees and the effective allocation of enterprise resources. Based on AHP and BP neural network theory, this paper constructs a comprehensive performance evaluation method for enterprises, AHP is used to calculate the weight of the index, and then the importance index is screened. The model proposes a conceptual model of comprehensive performance of manufacturing enterprises from the support layer, core layer, and promotion layer and constructs a manufacturing system from horizontal and vertical. The influencing factors of comprehensive performance solve the quantification problem of enterprise comprehensive performance evaluation and have obvious guiding value for the research on the integration mode and path of industrialization and industrialization of regional manufacturing enterprises. In the simulation process, the weight of each index in the evaluation system is first determined by the analytic hierarchy process; then the evaluation index membership score table is established, and fuzzy mathematics is used to calculate the expert’s score, so as to solve the problem caused by the intermediate value. The uncertainty caused by the jump is finally established by the analytic hierarchy process, and the neural network is used to simulate the sample. The experimental results show that by using AHP to collect training samples for neural network evaluation, the comprehensive performance evaluation system has good fitness and achieves the best comprehensive consideration of accuracy and training time when there are 17 hidden layer neurons. The maximum relative error is 1.64%, which is much lower than the general accuracy requirement of 5%, which effectively improves the performance and calculation accuracy of the network.

Evaluation and screening of technology start-ups based on PCA and GA-BPNN

“…Combined with the characteristics of BPNNM, and considering that the evaluation of TSs involves many and complex indicators, this paper finally selects BPNNM for evaluating TSs, and drew on the studies of related scholars. For example, Shu & Xu [ 36 ] combined multi-level dynamic fuzzy evaluation with BPNN, used multi-layer hidden layer neural network structure and BP algorithm to train the network, established an evaluation decision support system, and confirmed the feasibility of this model; Chen [ 37 ] used the AHP to calculate the index weights and used the BPNN method to simulate the samples, proving that the prediction accuracy was higher than that of the traditional prediction method.…”