Application research of grey fuzzy evaluation method in enterprise product reputation evaluation

Zhou, Li; Liu, Bisong; Zhao, Yan; Jiang, Zhou

doi:10.1016/j.procir.2019.05.014

Cited by 8 publications

(9 citation statements)

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“…Taking G1 enterprise as an example, the dimensionality reduction model is used to process the enterprise evaluation index data samples, and the eigenvalues and cumulative Literature [7]model Literature [8]model Literature [9]model Literature [10]model Literature [11]model This model contribution rate of the evaluation index data samples at each criterion level in the dimensionality reduction process are shown in Figures 3 and 4, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…However, the evaluation accuracy decreases due to the incomplete index system. Zhou et al [9] evaluated the enterprise product reputation more objectively and comprehensively by establishing the enterprise product reputation evaluation index system and combining the grey theory and fuzzy analysis method to evaluate the enterprise product reputation. is model can reduce the evaluation error rate caused by subjective factors, but it has the problem of high error rate in the calculation of evaluation weight.…”

Section: Introductionmentioning

confidence: 99%

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Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Li¹,

2022

Security and Communication Networks

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Enterprise core competence is closely related to enterprise management performance, and it is important to evaluate enterprise management performance. However, the current enterprise management performance evaluation model has the problems of high eigenvalues of sample data, low cumulative contribution and correlation, high error rate in the calculation of business management performance evaluation index weights, low evaluation accuracy, and long evaluation time. Therefore, the enterprise management performance evaluation model using improved fuzzy clustering algorithm in Internet of things (IoT) networks is proposed. First, in the IoT architecture, the enterprise management performance evaluation index system is established by using the balanced scorecard theory. Second, the evaluation index system is reduced in dimensionality by combining principal component analysis and kernel-independent component analysis, the fuzzy C-mean clustering algorithm based on the objective function is designed, and finally, the improved fuzzy clustering algorithm is obtained to establish the enterprise management performance evaluation model, the reduced evaluation index system is input, and the evaluation results are output. The results show that the sample data eigenvalue of this model is low. The maximum error rate of weight calculation is 2.3%, the accuracy is always more than 95%, and the average value of evaluation time is 0.57 s, which effectively realize enterprise management performance evaluation in IoT networks.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Li¹,

2022

Security and Communication Networks

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“…The BP neural network contains three layers: the input layer, the hidden layer, and the output layer. The three-layered network can apply to any nonlinear mapping and prevent the model from falling into local solutions due to excessive complexity [12]. The three-layered BP neural network designed here is shown in Fig 1. The detailed algorithm flows are as follows [13]:…”

Section: Enterprise's Performance Appraisal Methods Based On Bp-ga Modelmentioning

confidence: 99%

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Luo¹,

Da²

2021

PLoS ONE

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The purpose is to study the performance compensation of the bid purchased during the mergers and acquisitions (M&A) process. An intelligent model of enterprise performance appraisal is built to analyze the performances of the acquired enterprises. First, the evaluation indicators of enterprise performance are selected from both financial and non-financial aspects. An enterprise performance appraisal model is established based on the neural networks and optimized by the factor analysis method and Genetic Algorithm (GA). The principal factors affecting enterprise performance are analyzed. Then the M&A parties’ performances during the M&A commitment period under the earnings compensation mechanism are analyzed quantitatively. Corresponding hypotheses and evaluation indicators are established. Mean test results and regression analyses demonstrate that the hypotheses proposed are valid under particular circumstances. Introducing the earnings compensation mechanism during the M&A process can improve the enterprise performance effectively so that the earnings forecasted in the commitment period are significantly higher than the historical profitability. Hence, the earnings compensation mechanism plays a positive role in guiding enterprise performance. Comparison with models proposed in previous research reveals that the output error ratio of the designed corporate performance evaluation model is 1.16%, which can effectively evaluate corporate performance. The above results provide a reference for studying the impact of the earnings compensation mechanism on enterprise performance during the M&A process.

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“…Li et al [9], improved the method of XGBoost algorithm and proposed fuzzy XGBoost on the basis of adding fuzzy membership degree. Zhou et al [10] also proposed some solutions on fuzzy algorithm. Zhang et al [11,12] put forward a large-sample mixed credit evaluation model based on similar sample merge and a three-stage mixed credit evaluation model based on multiattribute subset selection strategy, respectively, in 2018 and 2019.…”

Section: Introductionmentioning

confidence: 99%

Enterprise Credit Security Prediction and Evaluation Based on Multimodel Fusion

Zhang

Zhao

2022

Security and Communication Networks

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Based on the industry data and enterprise data from tens of thousands of small and medium-sized enterprises, a deep learning and machine learning model of credit prediction is constructed through the division of data sets, processing, and integration of models. At first, with the help of two characteristic selection methods, several subsets separated from the dataset are analyzed based on convolutional neural network as coarse prediction. Then, combined with the tree model, the precise prediction is further made for the enterprise credit evaluation. Finally, the model fusion is carried out to obtain high-precision results. In the simulation experiment, this paper takes a data set of 14,366 small and medium-sized enterprise credit evaluations as the analysis samples to verify the results. The accuracy of the model is 97%, which is far more than 93% of single model with metadata set.

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Application research of grey fuzzy evaluation method in enterprise product reputation evaluation

Cited by 8 publications

References 0 publications

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Enterprise Management Performance Evaluation Model Using Improved Fuzzy Clustering Algorithm in IoT Networks

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Enterprise Credit Security Prediction and Evaluation Based on Multimodel Fusion

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