Implications of System Identification Techniques on ANFIS E-learners Activities Models-A Comparative Study

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dc.contributor.authorIsiaka, Mope Rafiu
dc.contributor.authorOmidiora Elijah Olusayo
dc.contributor.authorOlabiyisi Stephen O.
dc.contributor.authorOkediran Oladotun O.
dc.contributor.authorBabatunde Ronke Seyi
dc.date.accessioned2026-05-16T19:54:26Z
dc.date.available2026-05-16T19:54:26Z
dc.date.issued2016-02-21
dc.description.abstractEfficient e-learners activities model is essential for real time identifications and adaptive responses. Determining the most effective Neuro- Fuzzy model amidst plethora of techniques for structure and parameter identifications is a challenge. This paper illustrates the implication of system identification techniques on the performance of Adaptive Network based Fuzzy Inference System (ANFIS) E-learners Activities models. Expert knowledge and Historical data were used to formulate the system and their performances were compared. Similarly, comparison was made between memberships functions selected for Historical data identification. The efficiencies of the simulated models in MATLAB editor were determined using both classification uncertainty metrics and confusion matrix–based metrics. The classification uncertainty metrics considered are Mean Absolute Error (MAE) and Root Mean-Squared Error (RMSE). The confusion matrix-based metrics used are Accuracy, Precision and Recall. It was discovered that the model based on Experts Knowledge after training outperformed those based on Historical Data. The performances of the Membership Functions after ranking are Sigmoid, Gaussian, Triangular and G-Bells respectively.
dc.identifier.issn20254-7390
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/7267
dc.language.isoen
dc.publisherTransactional on Machine Learning and Artificial Intelligence
dc.titleImplications of System Identification Techniques on ANFIS E-learners Activities Models-A Comparative Study
dc.typeArticle
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