Name
Abstract | ||
|---|---|---|
The negation of probability distribution becomes an important topic since some problems are burdensome to deal with directly. Inspired by Yager's negation of probability distribution, an extension model to measure the negation of a probability distribution is proposed using the idea of a nonextensive statistic based on Tsallis entropy. Proofs show that the proposed extension of negation of probability distribution converges to the maximum Tsallis entropy. The proposed model may extend Yager's method to consider the influences of the correlations in a system, which gives the different convergent routes. Some numerical simulation results are used to illustrate the effectiveness of the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1002/int.22198 | INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS |
Keywords | Field | DocType |
entropy, negation, probability distribution, Tsallis entropy | Data mining,Discrete mathematics,Negation,Probability distribution,Tsallis entropy,Mathematics | Journal |
Volume | Issue | ISSN |
35 | 1 | 0884-8173 |
Citations | PageRank | References |
3 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Zhang | 1 | 3 | 0.36 |
| Ruqin Liu | 2 | 3 | 0.36 |
| Jianfeng Zhang | 3 | 4 | 1.85 |
| Bingyi Kang | 4 | 20 | 3.55 |