A Parallel Multi-Classifiers System for Mechanical Fault Recognition

Boukari, Karima; Farah, Lotfi

doi:10.1109/isaect.2018.8618783

Cited by 1 publication

(1 citation statement)

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“…With cascade models, the former classifier provides classification information for the latter classifier and guides the process of next classifier. With parallel models, various classifiers are independently designed, which are combined to fuse their respective results in an appropriate way, in order to attain a higher recognition rate [6]. For parallel classifiers, the classifier integration can be categorized into three types depending on the level of information utilized by them: the abstract level information-based integration (e.g.…”

Section: Introductionmentioning

confidence: 99%

A multi-state target recognition method based on multi-classifier fusion

Wang

Hong

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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A multi-classifier target recognition algorithm based on fuzzy pattern recognition and fuzzy comprehensive evaluation theory is proposed, where the base classifiers are deployed as fuzzy pattern recognition classifiers, and the secondary classifiers are based on fuzzy comprehensive evaluation theory. On the basis of time-frequency domain analysis of target signals, fuzzy theories such as fuzzy pattern recognition, decision-making and comprehensive evaluation are synthetically applied to carry out first-level evaluation, where the extracted feature parameters are used as the input of target base classifiers. With the evaluation results as the input of second-level evaluation, target state recognition is performed on the multi-state human targets behind the walls in the ultra-wideband radar sensor network based on fuzzy evaluation. The results show that the recognition can efficiently discern the Multi-status human being behind the wall.

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Section: Introductionmentioning

confidence: 99%