RETRACTED ARTICLE: Multi-level Dynamic Fuzzy Evaluation and BP Neural Network Method for Performance Evaluation of Chinese Private Enterprises

Ying, Shao‐Yao; Xu, Guanghua

doi:10.1007/s11277-018-5298-0

Cited by 13 publications

(8 citation statements)

References 15 publications

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“…Li [15] proposed an improved analytic hierarchy process-back propagation (AHP-BP) neural network and used it for the evaluation of corporate social responsibility (CSR) performances. Shu [16] combined a multi-level fuzzy evaluation and a BP neural network for the performance evaluation of private enterprises, so that the decision support system has the advantages of a neural network and multi-level dynamic evaluation simultaneously. Pei [17] combined the gray relational analysis method with a BP neural network to evaluate the resilience of enterprise safety production and provide a valuable reference for improving enterprise safety production.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Enterprise Collaboration Based on an Improved Elman Neural Network and AHP-EW

Zhang

Ding

et al. 2022

Applied Sciences

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In order to mitigate the influence of human subjectivity on indicator weights in the performance evaluation of enterprise collaboration, and explore the nonlinear relationship between the enterprise collaboration influencing factors and the evaluation results, this paper propose a combined performance evaluation model based on AHP-EW and an improved Elman neural network. Firstly, based on the characteristics of collaboration among manufacturing enterprises, the evaluation system for the collaborative performance of manufacturing enterprises is constructed from three dimensions. Moreover, this study combines subjective and objective weighting methods to obtain comprehensive weights that take into account both expert experience and objective information. Then, an improved Elman neural network is proposed and trained to predict and evaluate the collaborative performance indicator data, which greatly shortens the evaluation time and improves evaluation accuracy. The experimental results show that the proposed model has a faster convergence speed and higher accuracy, which will provide a valuable reference for decision making and the management of enterprise collaboration.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Enterprise Collaboration Based on an Improved Elman Neural Network and AHP-EW

Zhang

Ding

et al. 2022

Applied Sciences

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“…In 1985, Musser made a systematic and overall analysis of technological innovation [ 2 ]. Technological innovation is defined as technological innovation based on the novelty of its conception and its successful realization of meaningful discontinuous events as features [ 3 ]. Fu Jiaji believes that the technological innovation capability of an enterprise refers to the conditions and power of technological development and transformation that an enterprise exhibits in the process of technological innovation activities [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation Model of Agricultural Enterprise Technology Innovation Based on GA-BP Neural Network

Kang

Zhao

2022

Computational Intelligence and Neuroscience

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With the continuous development of my country’s economy and society, how to effectively evaluate the technical innovation performance of agricultural enterprises has become the focus of research. This paper firstly processes and analyzes the technical innovation performance data of agricultural enterprises, and then processes and converts the technical innovation performance data of agricultural enterprises; then, through the analysis of the technical innovation performance data of agricultural enterprises, the key characteristics of the technical innovation performance of agricultural enterprises are excavated. Finally, a performance evaluation model based on agricultural enterprise technology innovation is proposed, and the validity of the model is verified with examples.

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“…e comprehensive performance evaluation service has developed rapidly at home and abroad and has received more and more attention [1]. e essence of comprehensive performance evaluation is an innovative product produced by the combination of services, which is a multisubject and multiwin service model [2]. e huge e ect of this realistic multiwin model on the participants has made many companies ock to carry out this business.…”

Section: Introductionmentioning

confidence: 99%

BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance

Wenjing

2022

Mathematical Problems in Engineering

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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.

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RETRACTED ARTICLE: Multi-level Dynamic Fuzzy Evaluation and BP Neural Network Method for Performance Evaluation of Chinese Private Enterprises

Cited by 13 publications

References 15 publications

Performance Evaluation of Enterprise Collaboration Based on an Improved Elman Neural Network and AHP-EW

Performance Evaluation of Enterprise Collaboration Based on an Improved Elman Neural Network and AHP-EW

Performance Evaluation Model of Agricultural Enterprise Technology Innovation Based on GA-BP Neural Network

BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance

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