system identification

简明释义

系统识别

英英释义

例句

1.Using MATLAB, engineers can easily perform system identification to model their physical systems.

工程师可以使用MATLAB轻松执行系统辨识以对其物理系统建模。

2.The first step in system identification is collecting data from the system under various operating conditions.

在系统辨识中，第一步是从不同操作条件下收集系统数据。

3.Researchers often use system identification techniques to improve the performance of automated systems.

研究人员常常使用系统辨识技术来提高自动化系统的性能。

4.The accuracy of a system identification process greatly influences the effectiveness of the control strategy.

一个系统辨识过程的准确性极大地影响控制策略的有效性。

5.In control engineering, system identification is crucial for developing accurate models of dynamic systems.

在控制工程中，系统辨识 对于开发动态系统的准确模型至关重要。

作文

In the field of engineering and control theory, the concept of system identification plays a crucial role in understanding and modeling dynamic systems. System identification refers to the process of developing mathematical models of dynamical systems based on measured data from the system. This methodology is particularly important when the underlying physical processes are complex or not fully understood. By employing system identification, engineers can create accurate models that predict how a system will behave under various conditions, which is essential for designing effective control strategies.The process of system identification involves several key steps. First, data must be collected from the system during its operation. This data can include inputs, outputs, and any disturbances affecting the system. Once the data is gathered, it is analyzed to determine the appropriate model structure. This could range from simple linear models to more complex nonlinear representations, depending on the system's behavior. The next step involves estimating the parameters of the chosen model using statistical methods. Techniques such as least squares estimation or maximum likelihood estimation are commonly used to refine the model parameters so that the model closely fits the observed data.One of the significant advantages of system identification is its ability to improve the performance of control systems. For instance, in industrial automation, accurate models derived from system identification can lead to better predictive control, minimizing overshoot and settling time in response to changes in setpoints. Additionally, these models can help in fault detection and diagnosis, allowing for timely interventions before a system failure occurs.Moreover, system identification is not limited to traditional engineering applications. It has found its way into various fields, including economics, biology, and even social sciences, where systems exhibit dynamic behaviors. In economics, for example, system identification can be used to model market dynamics and predict economic trends based on historical data. In biology, it can help in understanding population dynamics and ecosystem interactions.Despite its numerous benefits, system identification also presents challenges. One of the primary difficulties is dealing with noise in the data, which can lead to inaccurate model estimations. Additionally, selecting the appropriate model structure is often a trial-and-error process that requires expertise and experience. Overfitting is another common issue where the model becomes too complex, capturing noise rather than the actual system behavior.In conclusion, system identification is a vital process in the analysis and control of dynamic systems. Its ability to create accurate mathematical models from empirical data empowers engineers and scientists across various disciplines to understand and predict system behavior effectively. As technology advances and data collection techniques improve, the importance of system identification will only continue to grow, paving the way for more sophisticated and efficient systems in the future.

在工程和控制理论领域，系统辨识的概念在理解和建模动态系统中发挥着至关重要的作用。系统辨识是指基于系统测量数据开发动态系统数学模型的过程。这种方法论在基础物理过程复杂或不完全理解时尤为重要。通过采用系统辨识，工程师可以创建准确的模型，以预测系统在各种条件下的行为，这对于设计有效的控制策略至关重要。系统辨识的过程包括几个关键步骤。首先，必须在系统运行期间收集数据。这些数据可以包括输入、输出以及影响系统的任何干扰。一旦数据被收集，就会对其进行分析以确定合适的模型结构。这可能从简单的线性模型到更复杂的非线性表示，具体取决于系统的行为。下一步涉及使用统计方法估计所选择模型的参数。常用的技术如最小二乘估计或最大似然估计，通常用于精细化模型参数，使模型与观察到的数据紧密匹配。系统辨识的一个显著优势是它能够提高控制系统的性能。例如，在工业自动化中，从系统辨识得出的准确模型可以导致更好的预测控制，最大限度地减少在设定点变化时的超调和稳定时间。此外，这些模型有助于故障检测和诊断，使得在系统故障发生之前能够及时干预。此外，系统辨识并不仅限于传统的工程应用。它已经进入了多个领域，包括经济学、生物学，甚至社会科学，在这些领域中，系统表现出动态行为。例如，在经济学中，系统辨识可以用于建模市场动态，并基于历史数据预测经济趋势。在生物学中，它可以帮助理解种群动态和生态系统相互作用。尽管有许多好处，系统辨识也面临挑战。其中一个主要困难是处理数据中的噪声，这可能导致模型估计不准确。此外，选择适当的模型结构通常是一个试错过程，需要专业知识和经验。过拟合是另一个常见问题，即模型变得过于复杂，捕获噪声而不是实际的系统行为。总之，系统辨识是动态系统分析和控制中的一个重要过程。它能够从经验数据中创建准确的数学模型，使工程师和科学家能够有效地理解和预测系统行为。随着技术的进步和数据收集技术的改善，系统辨识的重要性只会继续增长，为未来更复杂和高效的系统铺平道路。