Implications of System Identification Techniques on ANFIS E-learners Activities Models - A Comparative Study
DOI:
https://doi.org/10.14738/tmlai.41.1799Keywords:
Neuro Fuzzy Model, E-learners Activities, System Identification Technique, Dataset NormalizationAbstract
Efficient 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 membership functions. The models were simulated in MATLAB editor. The efficiency of the model was 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 outperform those based on Historical Data. The performances of the Membershp Functions at the identification of the Historical data respectively are Sigmoid, Gaussian, Triangular and G-Bells.
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