Learning Curve as a Knowledge-based Dynamic Fuzzy Set: A Markov Process Model

Halim, Siana and Intan, Rolly and Dewi, Lily Puspa (2019) Learning Curve as a Knowledge-based Dynamic Fuzzy Set: A Markov Process Model. [UNSPECIFIED]

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Abstract

In the fuzzy set theory introduced by Zadeh [15], membership degree of a fuzzy set is determined by a static membership function, i.e., it does not change over time. To improve this condition, then Wang introduced the dynamic fuzzy logic. In this concept, the membership degree of a fuzzy set is changing over time. Intan and Mukaido [5] introduced the knowledge-based fuzzy set, by means that the membership degree of a set is dependent on the knowledge of a person. Since the knowledge of a person is not static, the knowledge-based fuzzy set can be measured dynamically over time, so that we have the knowledge-based dynamic fuzzy set. In this paper, we approximate the learning process as a knowledge-based dynamic fuzzy set. We consider that the process of learning is dependent on the knowledge of a person from time to time so that we can model the learning process is a Markov process of dynamic knowledge. Additionally, using the triangular fuzzy number, we follow Yabuuchi et al. [14], for modeling the time difference in the dynamic knowledge fuzzy set as an autoregressive model of order one.

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