A neuro-genetic fuzzy system for the prediction of student’s academic performance

Ojekudo, Nathaniel Akpofure

doi:10.5958/2278-4853.2022.00269.5

Cited by 1 publication

(1 citation statement)

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“…The principal advantages reside in the excellent adaptivity, brilliant self-learning properties and important capabilities for extracting relations in complex data sets [18]. There are different variants of the ANN model, such as adaptive neuro-diffuse inference systems (ANFIS) [17,19,20], but Genetic Neuro-Fuzzy (GNF) techniques [21] stand out for their ability to analyze large volumes of data, identify complex patterns and generate accurate prediction models [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Classification of Belts Status Based on an Automatic Generator of Fuzzy Rules Base System

Marichal,

Hernández,

Ávila

et al. 2024

Applied Sciences

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The automation of maintenance is a growing field and consequently, predictive maintenance is achieving more importance. The main objective is to predict a breakage before it happens. In order to reach this, it is necessary to have an intelligent classification technique that analyzes the state of the key breakage elements and evaluates whether a replacement is necessary or not. This work presents a study to classify belts according to their state of use. For training, vibration data have been collected on a test bench using new belts, belts with half use and belts near the breaking point. The processing of these vibrations allows for extracting the characteristic parameters that can be related to its state of use, and then, after the initial analysis, these values are used as inputs for training the intelligent system. In particular, the Genetic Neuro-Fuzzy (GNF) technique has been chosen and, with the proposed algorithm, more detailed Fuzzy rules are obtained. Once the algorithm has been trained, it is possible to establish a relationship between the vibration shown by the belt and its state of use. The achieved results show that a good classifier has been built.

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