matrixes

简明释义

英[ˈmeɪtrɪsɪz]美[ˈmeɪtrɪsɪz]

n. 矩阵；模型；基质；子宫（matrix 的复数）

v. 组成矩阵（matrix 的三单形式）

英英释义

单词用法

同义词

反义词

例句

1.The paper presents a new method for selecting weighted matrixes-zero-point addition.

本文阐述了一种选取加权矩阵的新方法——零点追加法。

2.Consistent mass matrix and lumped mass matrixes are the common types of mass matrixes in structural dynamic analysis.

一致质量矩阵与集中质量矩阵是结构动力分析中常用的质量矩阵形式。

3.The results indicate that it is more accurate to estimate joint frequency by using probability matrixes than frequency matrixes.

结果表明，在计算联合频率中应用风向和风速概率矩阵比应用频率矩阵更准确、可靠。

4.To find out, he and his colleagues grew embryonic heart cells on matrixes of differing stiffness.

为了找到答案，他和他的同事在不同硬度的基质上培育胚胎心脏细胞。

5.Besides, the convergence of power sequences on positive transitive fuzzy matrixes is considered.

此外，考虑了正传递模糊矩阵的幂序列的收敛性。

6.As humans construct their housing, removing all vegetation from the land, energetic matrixes are moved in to replace nature's dream.

当人类建造他们的房屋时，移走土地上的所有植物，移入能量上的矩阵来取代大自然的梦想。

7.Disease machinery is a lower octave of the matrixes for all intents and purposes.

疾病机械在各种情况下都比矩阵低一个音阶。

8.The explicit formulas of matrixes composing the stiffness are given.

给出了构成刚度阵的各矩阵的显式表达式。

9.Proposes a new method of estimating Jacobian matrixes on-line for image-based robot visual servo systems.

研究基于图像的机器人视觉伺服技术中雅可比矩阵的在线估计方法。

10.In data analysis, we often work with multiple matrixes 矩阵 to represent complex datasets.

在数据分析中，我们经常使用多个matrixes 矩阵来表示复杂的数据集。

11.The architect used several matrixes 矩阵 to model the structural integrity of the building.

建筑师使用了几个matrixes 矩阵来模拟建筑的结构完整性。

12.In machine learning, we can manipulate matrixes 矩阵 to optimize our algorithms.

在机器学习中，我们可以操作matrixes 矩阵来优化我们的算法。

13.The scientist created matrixes 矩阵 to analyze the results of the experiment.

科学家创建了matrixes 矩阵来分析实验结果。

14.In computer graphics, transformations are often represented using matrixes 矩阵.

在计算机图形学中，变换通常用matrixes 矩阵表示。

作文

In the world of mathematics and computer science, the concept of matrixes (矩阵) plays a crucial role in various applications. A matrix is essentially a rectangular array of numbers, symbols, or expressions, arranged in rows and columns. This structure allows for efficient representation and manipulation of data, making it an invaluable tool in fields such as engineering, physics, and economics. One of the primary uses of matrixes (矩阵) is in solving systems of linear equations. For instance, consider the equations: 2x + 3y = 5 and 4x + y = 6. These can be represented in matrix form, allowing for straightforward calculations to find the values of x and y. By applying techniques such as Gaussian elimination or matrix inversion, we can arrive at solutions that would be cumbersome to derive using traditional algebraic methods. Moreover, matrixes (矩阵) are integral to computer graphics. In this domain, they are used to perform transformations such as translation, rotation, and scaling of images. When a graphic designer wants to rotate an image by a certain angle, they apply a transformation matrix that alters the coordinates of the image's pixels. This process is not only efficient but also enables real-time rendering of complex animations and visual effects in video games and movies. Another fascinating application of matrixes (矩阵) is in machine learning. Algorithms often rely on matrixes (矩阵) to represent datasets, where each row corresponds to a different observation and each column represents a feature. For example, in a dataset of housing prices, one column might represent the size of the house, while another could indicate the number of bedrooms. By manipulating these matrixes (矩阵), machine learning models can identify patterns and make predictions based on the input data. The versatility of matrixes (矩阵) extends beyond mathematics and computer science; they are also used in statistics to represent covariance and correlation among variables. Understanding the relationships between different variables is essential for making informed decisions in various fields, including finance and healthcare. Thus, matrixes (矩阵) serve as a foundation for many statistical models, enabling researchers to analyze data effectively. Furthermore, the study of matrixes (矩阵) leads to the exploration of eigenvalues and eigenvectors, which have significant implications in various scientific disciplines. For example, in quantum mechanics, the behavior of particles can be described using matrixes (矩阵), where eigenvalues correspond to observable quantities. This connection between matrixes (矩阵) and physical phenomena illustrates their importance in understanding the natural world. In conclusion, the concept of matrixes (矩阵) is fundamental to numerous disciplines, from mathematics and computer science to physics and statistics. Their ability to simplify complex problems and facilitate data manipulation makes them an essential tool for researchers, engineers, and analysts alike. As technology continues to advance, the relevance of matrixes (矩阵) will only grow, highlighting the need for a solid understanding of this mathematical concept.

在数学和计算机科学的世界中，matrixes（矩阵）的概念在各种应用中起着至关重要的作用。matrix 本质上是一个数字、符号或表达式的矩形数组，按行和列排列。这种结构允许有效地表示和操纵数据，使其成为工程、物理学和经济学等领域中不可或缺的工具。 matrixes（矩阵）的主要用途之一是求解线性方程组。例如，考虑方程：2x + 3y = 5 和 4x + y = 6。这些可以用 matrix 形式表示，从而允许简单的计算来找到 x 和 y 的值。通过应用高斯消元法或矩阵反演等技术，我们可以得出用传统代数方法推导出的解决方案，这将是繁琐的。 此外，matrixes（矩阵）在计算机图形学中是不可或缺的。在这个领域，它们用于执行图像的变换，如平移、旋转和缩放。当图形设计师想要将图像旋转一定角度时，他们会应用一个变换 matrix 来改变图像像素的坐标。这个过程不仅高效，而且使得在视频游戏和电影中实时渲染复杂动画和视觉效果成为可能。 matrixes（矩阵）的另一个迷人应用是在机器学习中。算法通常依赖于 matrixes（矩阵）来表示数据集，其中每一行对应不同的观察，每一列代表一个特征。例如，在一个房价数据集中，一列可能表示房子的大小，而另一列则可能指示卧室的数量。通过操纵这些 matrixes（矩阵），机器学习模型可以识别模式并根据输入数据进行预测。 matrixes（矩阵）的多功能性超越了数学和计算机科学；它们还用于统计学中表示变量之间的协方差和相关性。理解不同变量之间的关系对于在金融和医疗等各个领域做出明智的决策至关重要。因此，matrixes（矩阵）作为许多统计模型的基础，使研究人员能够有效地分析数据。 此外，matrixes（矩阵）的研究引出了特征值和特征向量的探索，这在各个科学学科中具有重要意义。例如，在量子力学中，粒子的行为可以用 matrixes（矩阵）来描述，其中特征值对应于可观察的量。这种 matrixes（矩阵）与自然现象之间的联系说明了它们在理解自然世界中的重要性。 总之，matrixes（矩阵）的概念是众多学科的基础，从数学和计算机科学到物理学和统计学。它们简化复杂问题和促进数据处理的能力使其成为研究人员、工程师和分析师的重要工具。随着技术的不断进步，matrixes（矩阵）的相关性只会增加，突显了对这一数学概念扎实理解的必要性。