The comprehensive evaluation of coordinated coal-water development based on analytic hierarchy process fuzzy

Liu, Xiaomin; Liu, Haiyan; Wan, Zheng; Pei, Haifeng; Fan, Huiling

doi:10.1007/s12145-020-00523-z

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…e fuzzy comprehensive evaluation method is mainly used to make an overall evaluation of the competitive ability of badminton athletes. rough the conversion of qualitative evaluation into quantitative evaluation, plus its clear and systematic results, a scientific comprehensive evaluation is finally obtained [30]. e research design framework of this study is shown in Figure 3.…”

Section: Fuzzy Comprehensive Evaluation Methodmentioning

confidence: 99%

[Retracted] Signal Recognition Based on APSO‐RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

Guo

Huang

2021

Computational Intelligence and Neuroscience

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The advanced analysis and research methods of big data will provide theoretical support for the integration of athletes’ talent training. The advanced technological methods of big data will also give full play to the advantages of tapping the potential of talents and actively improve the success rate of grassroots young athletes. This paper proposes an improved Adaptive Particle Swarm Optimization (APSO) algorithm for the optimization of radial basis function (RBF) neural network parameters. The basic structure of RBF neural network is introduced, and the influence of parameters on the performance of RBF neural network is analyzed. The optimization method of RBF neural network parameters is analyzed, and Particle Swarm Optimization (PSO) algorithm is selected as the parameter optimization method of RBF neural network. In addition, an improved APSO algorithm is proposed according to the advantages and disadvantages of PSO and compared with other PSO algorithms. Experimental results show that the improved PSO algorithm has better accuracy. The improved PSO algorithm is applied to the parameter optimization of RBF neural network, and the experimental results prove the superiority of the proposed method. By weighting the second-level indicators, the weights of the second-level indicators of athletes’ competitive ability are in order of skill, athletic quality, psychological ability, and artistic expression. Skills are the main factors that determine the level of competitive ability. Sports quality and psychological ability are important guarantees for supporting the normal performance of skills. Artistic expressiveness is a supplementary factor for competitive ability. The various elements cooperate with each other and interact with each other. The indicators do not exist alone but cooperate with each other to support the formation of the entire athletic ability system. In the content of the competitive ability index of excellent athletes, technical ability is the core, and sports quality, psychological ability, and artistic performance ability ultimately exist to serve the improvement of technical ability. The competition scores of the 100 athletes counted in this article are all above 9.0 points. The difference between APSO-RBF’s action quality scores of 100 athletes and the real value is less than 3%. In terms of movement difficulty, the APSO-RBF evaluated athletes’ scores are close to 1.65 points, which is basically the same as the real value.

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Section: Fuzzy Comprehensive Evaluation Methodmentioning

confidence: 99%

[Retracted] Signal Recognition Based on APSO‐RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

Guo

Huang

2021

Computational Intelligence and Neuroscience

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“…e comprehensive analysis results should be used to calculate the comprehensive score of each evaluation object, sorted by size, and select the best order. is paper mainly uses the weighted average principle to process the results and finally determines the relative position of the evaluation objects [26].…”

Section: Overview Of Fuzzy Comprehensive Evaluation Methodmentioning

confidence: 99%

[Retracted] Comprehensive Evaluation of BIM Calculation Quantity in Domestic Construction Engineering Based on Fuzzy Comprehensive Evaluation

Deng

Rao

Cai

2021

Computational Intelligence and Neuroscience

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Nowadays, with the leap-forward development of computer technology and the transformation of information technology management concepts, China’s construction industry is quietly entering the era of refined management. Accurate estimation and cost control have become among the key considerations of the construction industry. For the calculation of engineering quantity, there are already many software devices that can be used for the calculation of engineering structure quantity, which means that the incorrect operation of personnel has been reduced to some extent, improving the work efficiency and measurement accuracy. The purpose of this paper is to solve the problems of computational missing, computational errors, inefficiency, data loss, and repetitive system in traditional computing based on the advantages of BIM computing system, which provide a reliable basis for cost forecasting and control. At the same time, using BMI calculation system to solve the problem of steel reinforcement in construction engineering, as well as the use of personnel, the existing calculation software still needs a lot of time and energy. We proposed a comprehensive evaluation study of reinforcement calculation in domestic construction engineering BIM calculation system based on a fuzzy comprehensive evaluation. This paper first summarizes the BIM calculation system of construction engineering, uses fuzzy comprehensive evaluation system as an important evaluation index system in domestic construction engineering BIM calculation system, through the judgment of various factors affecting the actual effect of the calculation system, and uses the fuzzy evaluation system combined with a case to demonstrate the superiority of the proposed research. Therefore, through the above research and experiments, it is concluded that the research method of this paper solves many problems in the process of engineering structure reinforcement calculation and provides a good reference method for the establishment of comprehensive evaluation system of reinforcement calculation, as well as providing an effective validation for the widespread use of BIM technology in the construction industry. Finally, it is also beneficial for users to comprehensively evaluate the BIM calculation system of the construction industry and provide a basic reference condition for different industries to use and choose BIM calculation systems.

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“…e importance measurement method of evaluation indicators usually uses the method of 1-9 and its reciprocal for statistics, and the scale table is shown in Table 2 [16,17].…”

Section: Architectural Judgment Matrixmentioning

confidence: 99%

Research on Comprehensive Evaluation of Economic Management Performance Based on Improved Fuzzy Clustering Algorithm

Shi

2022

Security and Communication Networks

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In order to solve the problems of low recall rate and precision rate, high error rate, and long evaluation time in traditional evaluation methods, a comprehensive evaluation of economic management performance based on an improved fuzzy clustering algorithm is designed. The improved magnetic optimization algorithm was used to optimize the fuzzy C-mean algorithm, the improved fuzzy clustering algorithm was completed, and the improved fuzzy clustering algorithm was used to mine the economic management performance data. Using data mining findings and AHP’s weighting formula, a complete method for evaluating economic management effectiveness was developed. The BP neural network was improved using a genetic algorithm based on the index weight calculation findings, and the full-assessment model of economic management performance was constructed. Using this approach, it is possible to accurately and quickly assess the economic management performance of a company with a high rate of recall and accuracy; the error rate of a thorough assessment ranges between −3 percent and 4 percent; the average duration for an assessment is 0.81 seconds.

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The comprehensive evaluation of coordinated coal-water development based on analytic hierarchy process fuzzy

Cited by 5 publications

References 4 publications

[Retracted] Signal Recognition Based on APSO‐RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

[Retracted] Signal Recognition Based on APSO‐RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

[Retracted] Comprehensive Evaluation of BIM Calculation Quantity in Domestic Construction Engineering Based on Fuzzy Comprehensive Evaluation

Research on Comprehensive Evaluation of Economic Management Performance Based on Improved Fuzzy Clustering Algorithm

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