A New Integrated Approach for Risk Evaluation and Classification With Dynamic Expert Weights

Liu, Hu‐Chen; Chen, Xu-Qi; You, Jing; Li, Zhiwu

doi:10.1109/tr.2020.2973403

Cited by 36 publications

(9 citation statements)

References 57 publications

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“…e learning algorithm of CM-FNN adopts the more mature BP algorithm. In the process of network training that involves the initialization of weights and parameter adjustment, the traditional method initializes the weights by selecting random, as small as possible values, which easily leads to long network training time, too many iterations, and the existence of easy to fall into local optimal solutions [19]. In the current study, research scholars proposed optimizing the BP algorithm using genetic algorithm, particle swarm algorithm, annealing algorithm, and so on, but these algorithms are often too complicated in determining parameters and initialization.…”

Section: Fuzzy Neural Network Ice and Snow Tourismmentioning

confidence: 99%

[Retracted] Fuzzy Neural Network‐Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness

2021

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This paper researches and analyzes the evaluation of the competitiveness of ice and snow tourism, uses the improved fuzzy neural network algorithm to process the system flow diagram of ice and snow tourism development through the function and characteristics of the power system of ice and snow tourism, and finally selects more than 40 indicators of the three subsystems of resources, economy, and culture. Based on the construction of cloud fuzzy neural network model, the above method is used for experimental comparison analysis, and experiments are conducted through University of California Irvine (UCI) dataset and engineering examples to compare with the traditional cloud model, fuzzy neural network, and BP neural network to analyze the operation efficiency, accuracy rate, and several rules of the algorithm. Through the experimental comparative analysis, the cloud fuzzy neural network can fully take into account the randomness and fuzziness of the data, optimize the generation of cloud rules, avoid multidimensional rule disasters, and ensure the operational efficiency of the algorithm; the accuracy rate of the algorithm is improved relative to that of the traditional technology, and it applies to a variety of datasets. And the software is used to test the ice and snow tourism industry system dynamics model to realize the correctness and robustness testing of the model. After the constructed model can reflect the real situation within the error range, the final policy simulation of the model is carried out.

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Section: Fuzzy Neural Network Ice and Snow Tourismmentioning

confidence: 99%

[Retracted] Fuzzy Neural Network‐Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness

2021

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“…For example, Lo and Liou 27 applied the best-worst method (BWM) and probability-based grey relational analysis (GRA) to reduce information subjectivity. Furthermore, Liu et al 28 used hesitant uncertain linguistic Z numbers (HULZNs) to improve the traditional FMEA method. Nie et al 29 combined Bayesian fuzzy assessment number with GRA-TOPSIS method.…”

Section: Literature Reviewmentioning

confidence: 99%

Survey of FMEA methods with improvement on performance inconsistency

Ouyang

Ling

Han

et al. 2021

Quality & Reliability Eng

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Failure mode and effects analysis (FMEA) is an effective risk assessment tool for detecting and reducing possible risks during a manufacturing process. However, traditional FMEA has some shortcomings when used in the real world. In recent years, improved FMEA approaches have been proposed to eliminate the inherent shortcomings of FMEA, but the risk ranking result obtained from those FMEA approaches may be inconsistent. Therefore, this paper integrates six FMEA approaches by using an ensemble learning technique to obtain comprehensive and reliable rankings for failure modes. Data from the assembly process of spark plugs are used to check the performance of the proposed method. Meanwhile, a comparation is designed to illustrate that the proposed FMEA method can not only obtain reliable results, but also provide meaningful management insights for practitioners.

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“…Liu et al contributed with several studies on the failure mode and effects analysis [31]. They improved failure mode analysis using two-dimensional uncertain linguistic variables and alternative queuing [32] and proposed a novel approach combining HULZNs and DBSCAN algorithms to assess and cluster the risk of failure modes [33]. They evaluated the feasibility of the proposed approaches in real use-case scenarios, showing the ability to classify failure modes in complex and uncertain conditions.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning

Cotroneo,

De Simone,

Liguori

et al. 2021

Preprint

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Identifying the failure modes of cloud computing systems is a difficult and time-consuming task, due to the growing complexity of such systems, and the large volume and noisiness of failure data. This paper presents a novel approach for analyzing failure data from cloud systems, in order to relieve human analysts from manually fine-tuning the data for feature engineering. The approach leverages Deep Embedded Clustering (DEC), a family of unsupervised clustering algorithms based on deep learning, which uses an autoencoder to optimize data dimensionality and inter-cluster variance. We applied the approach in the context of the OpenStack cloud computing platform, both on the raw failure data and in combination with an anomaly detection pre-processing algorithm. The results show that the performance of the proposed approach, in terms of purity of clusters, is comparable to, or in some cases even better than manually fine-tuned clustering, thus avoiding the need for deep domain knowledge and reducing the effort to perform the analysis. In all cases, the proposed approach provides better performance than unsupervised clustering when no feature engineering is applied to the data. Moreover, the distribution of failure modes from the proposed approach is closer to the actual frequency of the failure modes.

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A New Integrated Approach for Risk Evaluation and Classification With Dynamic Expert Weights

Cited by 36 publications

References 57 publications

[Retracted] Fuzzy Neural Network‐Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness

[Retracted] Fuzzy Neural Network‐Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness

Survey of FMEA methods with improvement on performance inconsistency

Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning

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