Affiliations: [a] Computer Science Department, Chuka University, Chuka, Kenya
| [b] School of Computing and Information Technology, JKUAT University, Nairobi, Kenya
| [c] School of Computing and Informatics, MUST University, Meru, Kenya
Correspondence:
[*]
Corresponding author. Kevin Otieno Gogo, Tel.: +254721657982; E-mail: E-mail: [email protected] and E-mail: [email protected].
Abstract: Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases.
Keywords: Interval type 2 gaussian fuzzy logic, linear regression predictive models, intelligent system models, knowledge-bases
