使用期限租赁和永久
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Lego
Robotics and Mechatronics
Examples: Arduino, LEGO® MINDSTORMS®
Matlab在机电一体化的应用
Developing mechatronic systems requires integrating mechanical, electrical, control, and embedded software subsystems. Model-Based Design and MathWorks products enable you to design and simulate all of these domains in a single environment.
Through desktop and real-time simulations, you can:
Predict and optimize system performance
Understand and manage complex system interactions
Detect design inefficiencies and integration errors early during development
Verify and test mechatronic systems such as motor control using fewer hardware prototypes.
Eliminate manual coding errors by automatically generating code from the simulation model
金融预测和建模
运动MATLAB的应用程序，只需移动鼠标并点击，，即可导入时间序列的数据完成计量经济模型的拟合（例如 ARMA、ARIMA、GARCH、EGARCH、GJR）或机器学习算法。
MATLAB提供了DSGE 模型界面来预测关键经济变量的。
根据 Nelson-Siegel 或 Svensson 模型估算的参数进行利率建模和预测。
品定价
使用 MATLAB 中的 Monte Carlo 模拟计算变异期权（exotic option）的价格和敏感度指标变量，远远快于其在 Visual Basic、R 和 Python 中的运算速度。
选择多种方法（例如闭合方程、二叉树、三叉树和随机波动模型）来对期权进行定价，包括欧式期权、美式期权、亚式期权、障碍期权、利率封顶期权、利率保底期权、互换期权和多基础资产品。
并行运行计算密集型应用程序或者将它们部署到 GPU。
与 Numerix 进行交互。
保险和精算学
分析大数据集，创建定制的精算模型，并使用并行化轻松加快模拟运算速度。
以 MATLAB 为平台，针对 Solvency II 构建自定义风险模型。
对多种保险产品（如变额年金、低收益保证期权、定期保险和养老保险）进行定价。
Access and Analyze Data
MATLAB data analysis products let you access, visualize, and analyze data within a single environment. You can interactively explore data, build customized analyses, and share your discoveries with others through reports, published code, 3D visualizations, or as complete applications.
Develop Machine Learning Algorithms
Machine learning algorithms use computational methods to "learn" information directly from data without assuming a predetermined equation as a model. They can adaptively improve their performance as you increase the number of samples available for learning. Machine learning algorithms are used in applications such as speech and facial recognition. They are used in big data applications that have large datasets with many predictors (features) and are too complex for simple parametric models.
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