General Regression Neural Network and Artificial-Bee-Colony Based General Regression Neural Network Approaches to the Number of End-of-Life Vehicles in China

Fang, Xiaoli; Ni, Songyuan; Li, Hongliang; Zhou, Xuesheng

doi:10.1109/access.2018.2814054

Cited by 13 publications

(13 citation statements)

References 31 publications

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“…ABC is a mature algorithm which has been widely applied in solving numerous optimization problems due to its prominent convergence characteristics [37,40]. In order to obtain the highest operating efficiency and the highest diagnostic accuracy, we utilized the ABC algorithm to optimize the SVM parameters, and constructed the ABC–SVM based transformer fault diagnosis model.…”

Section: Fault Diagnosis Model Based On Abc–svmmentioning

confidence: 99%

“…Accounting for the relatively low request of the speed and the highlighted accuracy and reliability, a 3-stage GA–SA–SVM selection model which combined a genetic algorithm (GA) [35] and simulated annealing (SA) algorithm [36] with SVM was utilized to complete the selection of OFC. In addition to these, the artificial bee colony (ABC) algorithm [37], which has the fastest iteration speed and the highest search efficiency, was exploited in the diagnostic stage. The entire system has been fully realized, with the accuracy of its result reaching 92%.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

et al. 2018

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Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.

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Section: Fault Diagnosis Model Based On Abc–svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

et al. 2018

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“…In conclusion, the whole automotive RSC process shows high uncertainty in terms of time, quantity, and quality. In certain developing nations, the ELVs quantity is anticipated in a conventional way with limited accuracy, using an empire multiply factor of 5% -8% of the total number of vehicles (Xin et al, 2018). In this case, most ELVs tend to flow into illicit markets or recycle dealers through various informal channels rather than being returned it to original equipment manufacturers, leading to resource waste, safety hazards, and unsustainable development (Li et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Number of End-of-Life Vehicles: State of the Art Report

2022

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Academics and practitioners have shown a growing interest in automobile reverse supply chain (RSC) management as a result of the rise of circular economy and the development of Industry 4.0. Accurate quantity prediction enhances the efficiency of all decision levels in automobile RSC, not only the recovery of end-of-life vehicles (ELVs). Therefore, a comprehensive state-of-the-art review, evaluating ELVs quantity forecasting methodologies and summarizing the main variables influencing forecasting outcomes, is conducted to throw shed light on future research directions.

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“…With the current resource scarcity and increasing environmental pressure, the ELV recycling industry is increasingly concerned by academia and business [2]. Lots of micro-researches of ELV recycling network and recycling technology through the algorithm and experimental simulation of partially to optimize a link in the industry [3]- [5]. However, ELV recycling and dismantling enterprises require a reasonable facility layout, which directly relates to the dismantling efficiency of ELV and the quality of the recycling products caused by advanced disassembly technology [6], [7].…”

Section: Introductionmentioning

confidence: 99%

End of Life Vehicle Disassembly Plant Layout Evaluation Integrating Gray Correlation and Decision Making Trial and Evaluation Laboratory

Zhang

Liu³

et al. 2020

IEEE Access

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End of life vehicle (ELV) is a significant renewable resource with enormous economic value and environmental value. This paper introduced a novel approach that combines the gray correlation and Decision Making Trial and Evaluation Laboratory (DEMATEL) with an interactive technique in order to consider both quantitative and qualitative features simultaneously in ELV plant facility layout problem. The gray interval number of gray correlation is used as the expert's evaluation score of the impact factor of the ELV dismantling plant, DEMATEL constructs a direct impact matrix by analyzing the causal relationship between various influencing factors in the system. The proposed approach is applied to the case of a realistic ELV dismantling plant, and the evaluation framework for an ELV plant layout alternatives and design assessment analysis also conducted. The results show that it provides a systematic decision support tool and a feasible approach for evaluation of ELV disassembly plant layout alternatives.

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General Regression Neural Network and Artificial-Bee-Colony Based General Regression Neural Network Approaches to the Number of End-of-Life Vehicles in China

Cited by 13 publications

References 31 publications

A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

Forecasting the Number of End-of-Life Vehicles: State of the Art Report

End of Life Vehicle Disassembly Plant Layout Evaluation Integrating Gray Correlation and Decision Making Trial and Evaluation Laboratory

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