Optimising the parameters of a RBFN network for a teaching learning paradigm

Pamela Chaudhury (School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) University)
Hrudaya Kumar Tripathy (School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 15, No 1: July 2019

Academic performance of students has been a concern worldwide. Despite efforts made by educational institutions there has been a rise in poor academic performance. In our research study we have proposed a model to pre-determine the academic performance of students using a Radial Basis Function network (RBFN) using primary data. The proposed model has been developed by using algorithms like differential evolution (DE) and teaching learning based optimization (TLBO). This model can be used by academic institutions to identify the academically weaker students and take preventive steps to reduce the number of academic failures.