JACIII Vol.23 No.2 pp. 219-228
doi: 10.20965/jaciii.2019.p0219


Parameter Learning for an Intuitionistic Fuzzy Belief Rule-Based Systems Based on Weight and Reliability

Yanni Wang*,**

*China Electronics Standardization Institute
No.1 Andingmen East Street, Dongcheng District, Beijing 100007, China

**Beihang University
No.37 Xueyuan Road, Haidian District, Beijing 100083, China

December 12, 2018
January 8, 2019
March 20, 2019
fuzzy, belief, reliability, weight, parameter learning

The intent of the parameter learning is to ensure the accuracy of intuitionistic fuzzy belief rule-based systems (IFBRBSs) considering both weight and reliability. The main contribution is that distinguish reliability and weight respectively treated as intrinsic and extrinsic properties of evidence. A parameter learning method considering both reliability and weight determined by internal and external conflicts (PL-RW-IEC) is proposed. Evidence reasoning with reliability and weight is introduced as a basis of the learning process. After learning, the mean square error (MSE) between the real output and the simulated output decreases 75 times. Compared to the parameter learning considering both reliability and weight determined by Dempster’s conflict (PL-RW-DC) and compared to the parameter learning not considered reliability (PL-NR), the PL-RW-IEC method gets the most accurate result according to the MSE.

The parameter learning of IFBRBSs

The parameter learning of IFBRBSs

Cite this article as:
Y. Wang, “Parameter Learning for an Intuitionistic Fuzzy Belief Rule-Based Systems Based on Weight and Reliability,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.2, pp. 219-228, 2019.
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Last updated on May. 19, 2024