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相关概念视频

Weighted Mean00:57

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
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When understanding the effects of multiple forces acting on an object, vector addition is a crucial concept to grasp. This mathematical concept can be used to calculate the net force acting on an object when two or more forces are involved.
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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混沌融合基于突变的载体加权平均值 线性天线阵列优化算法

Zhuo Chen1, Yan Liu1, Liang Dong2

  • 1School of Physics and Electronic Information, Yunnan Normal University, Kunming 650504, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
概括

本研究介绍了一种基于混沌融合突变的加权平均向量算法,用于优化不均距离的线性数组. 与现有技术相比,新方法实现了优异的侧叶水平降低和深度零转向.

关键词:
线性天线阵列是一种线性天线阵列.进行元启发式优化优化.没有方向盘的方向盘.模式合成模式合成侧叶片抑制抑制 侧叶片抑制

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科学领域:

  • 优化算法 优化算法
  • 天线阵列合成
  • 信号处理 信号处理

背景情况:

  • 优化天线阵列合成对于高级应用程序至关重要.
  • 现有的方法经常与复杂的约束作斗争,并实现深度零值.
  • 矢量的加权平均值 (INFO) 框架为数组合成提供了一个有希望的基础.

研究的目的:

  • 提出一种先进的优化算法,用于合成不均距离的线性数组.
  • 通过新的机制来增强矢量的加权平均值 (INFO) 框架.
  • 为了解决最大限度地降低侧叶水平 (SLL) 和同时实现深度零转向的限制问题.

主要方法:

  • 开发了基于突变的加权平均向量的混沌融合算法.
  • 整合了良好的点设置初始化,以改善人口覆盖率.
  • 利用基于混沌的适应性参数化用于勘探-开发平衡.
  • 实施正常云突变以保持多样性并防止过早的融合.
  • 制定了数组因子 (AF) 优化作为惩罚函数的受约束问题.

主要成果:

  • 拟议的算法在各种数组合成任务中始终实现了较低的峰值SLL和更准确的零值.
  • 与基准metaheuristics相比,证明了更快,更稳定的趋同.
  • 在深度零压抑和SLL减少方面,它在增强的IWO上表现出强大的优势.
  • 在一个工程实例中,在104°达到大约-32.30dB的SLL和在104°达到-125.1dB的深空.
  • 确认了CEC2020现实世界受约束问题的强有力的融合和竞争力统计排名.

结论:

  • 混沌融合基突变加权平均向量算法是用于间距不均的线性数组合成的高效技术.
  • 基于混乱的机制和云突变的集成显著提高了优化性能.
  • 该算法为具有深度零要求的复杂数组合成问题提供了强大而优异的解决方案.