A new approach to uncertainty description through accomplishment membership functions

Ibarra, Luis; Ponce, Pedro; Molina, Arturo

doi:10.1016/j.eswa.2015.05.021

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…This article uses the fuzzy distribution determination method [6][7][8] to construct a fuzzy relationship matrix. For different evaluation objects, it is necessary to select the appropriate expression of the membership function [9][10][11] for fuzzy comprehensive evaluation.…”

Section: Comprehensive Safety Evaluation In the Fuzzy Modementioning

confidence: 99%

“…The initial value of the fuzzy relation matrix is the Zero matrix. After mapping to the system evaluation set, the formula for generating non-zero elements in the fuzzy relationship matrix is shown in Equations ( 5) to (8). As shown in Figure 3, the evaluation values of indexes located in different distribution intervals v m (star-shaped symbols in Figure 3) will generate two adjacent fuzzy relationship matrix elements.…”

Section: Comprehensive Safety Evaluation In the Fuzzy Modementioning

confidence: 99%

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Safety Evaluation of Metal Structures of Quayside Container Cranes Based on Combination Weighting and Fuzzy Comprehensive Evaluation

Chen,

Ding

2024

Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15–17, 2023, N

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To improve the accuracy and reliability of metal structure safety evaluation for quayside container cranes, this article constructs a fuzzy comprehensive evaluation method based on a membership function model, proposes and adopts a subjective and objective weight combination weighting method that combines CRITIC objective weight method, and AHP subjective weight method, and finally provides a system safety evaluation method for quayside container crane metal structures. Especially, the datadriven safety evaluation system for the metal structure of Quayside container cranes has been developed, which is convenient for large-scale promotion and application. At the same time, the engineering application results show that the combination weighting method reduces the extreme value impact caused by subjective judgment, and takes into account the differences and correlations between different indexes. The evaluation results obtained by this method are more accurate and safe.

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Section: Comprehensive Safety Evaluation In the Fuzzy Modementioning

confidence: 99%

Section: Comprehensive Safety Evaluation In the Fuzzy Modementioning

confidence: 99%

Safety Evaluation of Metal Structures of Quayside Container Cranes Based on Combination Weighting and Fuzzy Comprehensive Evaluation

Chen,

Ding

2024

Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15–17, 2023, N

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show abstract

“…Along with issue such as; rule base and training methods; ANFIS also suffer from uncertainty. Therefore, techniques like fuzzy logic have been applied because fuzzy logic with the help of membership function is capable to describe uncertainty [15]. As ANFIS also practice fuzzy logic, therefore correct choice of membership functions is most important factor in building the ANFIS model.…”

Section: Comparative Study Of Anfismentioning

confidence: 99%

A Modified Neuro-Fuzzy System Using Metaheuristic Approaches for Data Classification

Salleh¹,

Talpur²,

HussainTalpur³

2018

Artificial Intelligence - Emerging Trends and Applications

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The impact of innovated Neuro-Fuzzy System (NFS) has emerged as a dominant technique for addressing various difficult research problems in business. ANFIS (Adaptive Neuro-Fuzzy Inference system) is an efficient combination of ANN and fuzzy logic for modeling highly non-linear, complex and dynamic systems. It has been proved that, with proper number of rules, an ANFIS system is able to approximate every plant. Even though it has been widely used, ANFIS has a major drawback of computational complexities. The number of rules and its tunable parameters increase exponentially when the numbers of inputs are large. Moreover, the standard learning process of ANFIS involves gradient based learning which has prone to fall in local minima. Many researchers have used meta-heuristic algorithms to tune parameters of ANFIS. This study will modify ANFIS architecture to reduce its complexity and improve the accuracy of classification problems. The experiments are carried out by trying different types and shapes of membership functions and meta-heuristics Artificial Bee Colony (ABC) algorithm with ANFIS and the training error results are measured for each combination. The results showed that modified ANFIS combined with ABC method provides better training error results than common ANFIS model.

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Safety state evaluation and risk management of navigation environment in harbour waters based on Bayesian network

Chen

Zhang

et al. 2016

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE)

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A new approach to uncertainty description through accomplishment membership functions

Cited by 5 publications

References 44 publications

Safety Evaluation of Metal Structures of Quayside Container Cranes Based on Combination Weighting and Fuzzy Comprehensive Evaluation

Safety Evaluation of Metal Structures of Quayside Container Cranes Based on Combination Weighting and Fuzzy Comprehensive Evaluation

A Modified Neuro-Fuzzy System Using Metaheuristic Approaches for Data Classification

Safety state evaluation and risk management of navigation environment in harbour waters based on Bayesian network

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