生物模拟模型用于计算缺失数据的归算和对对比矩阵中的不一致性减少.
Waldemar W Koczkodaj1, Witold Pedrycz2, Alexander Pigazzini3
1Department of Computer Science, Laurentian University, Sudbury, Ontario, Canada.
PloS one
|August 7, 2025
概括
本研究介绍了一种生物模拟模型,用于对对比矩阵中的数据归算和不一致性减少. 这种新的方法有效地填补了缺少的数据,并提高了矩阵一致性,提供了一个强大的解决方案.
科学领域:
- 决策分析 决策分析
- 计算建模计算建模
- 生物仿真算法的算法
背景情况:
- 双对比矩阵对于决策至关重要,但通常包含缺失的数据和不一致性.
- 处理这些问题的现有方法可能很复杂,可能并不总能产生可靠的结果.
研究的目的:
- 开发一种新的仿生模型,以解决缺失的数据归算问题,并减少对对比矩阵中的不一致性.
- 模拟生物再生过程,用于数据矩阵修复和优化.
主要方法:
- 一种生物模拟再生方法,灵感来自生物过程:损伤识别,细胞增殖 (数据归算) 和稳定 (一致性优化).
- 采用代算法来纠正不一致性,并计算对对比矩阵中缺少的数据归算.
主要成果:
- 生物模拟模型成功计算了缺失的数据归算.
- 这种方法有效地减少了不一致性,导致一个更全球一致的对对比比较矩阵.
- 该方法证明了稳定性和可靠的趋同到一个一致的解决方案.
结论:
- 拟议的生物模拟模型为数据归算和对对比矩阵中的不一致性减少提供了一种有效和强大的方法.
- 这种新的方法提供了一种可靠的方式来实现决策矩阵的全球一致性.
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