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In view of the problem that traditional buffering operators can’t fine-adjust the action intensity, which leads to too strong or too weak buffering effect (the buffering effect of \(n\) order buffering operators is too weak, and the buffering effect of \(n + 1\) order buffering operators may be too strong), fractional weakening buffering operators are studied in this chapter. The new information priority of classical weakening buffering operator, variable weight weakening buffering operator and ordinary strengthening buffering operator is proved by matrix disturbance theory. The relationship between sample size and buffering effect is discussed.

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Grey Prediction Model Based on Fractional Order Buffering Operator

  • 章节

  • 发表日期：

    2025年

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  • Lifeng Wu,
  • Yan Chen

摘要

In view of the problem that traditional buffering operators can’t fine-adjust the action intensity, which leads to too strong or too weak buffering effect (the buffering effect of \(n\) order buffering operators is too weak, and the buffering effect of \(n + 1\) order buffering operators may be too strong), fractional weakening buffering operators are studied in this chapter. The new information priority of classical weakening buffering operator, variable weight weakening buffering operator and ordinary strengthening buffering operator is proved by matrix disturbance theory. The relationship between sample size and buffering effect is discussed.