机器学习和系统识别过程系统工程动态的比较研究
Akhil Ahmed1, Ehecatl Antonio Del Rio-Chanona1, Mehmet Mercangöz1
1Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2BX, U.K.
Industrial & engineering chemistry research
|March 3, 2025
概括
本研究对过程系统工程 (PSE) 中动态系统建模的传统和机器学习 (ML) 模型进行了基准分析. 具有平衡复杂性的ML模型,像树集,提供卓越的预测准确性和效率.
科学领域:
- 过程系统工程 (PSE)
- 数据驱动建模数据驱动建模
- 机器学习 (ML) 是指机器学习.
背景情况:
- 传统的系统识别方法在复杂的动态系统中面临着挑战.
- 现代机器学习 (ML) 提供了PSE中改进数据驱动建模的潜力.
- 将 ML 整合到 PSE 中需要强大的框架和评估策略.
研究的目的:
- 为了对PSE应用程序的传统系统识别与ML模型进行比较.
- 评估MLOps启发的工具对系统识别任务的有效性.
- 提供对PSE最佳模型选择和绩效评估的见解.
主要方法:
- 利用了AutoSID,这是一个由机器学习操作 (MLOps) 启发的自动化框架.
- 在11个PSE案例研究中比较了12个不同的模型架构 (系统识别,ML,深度学习).
- 采用了4个模型搜索/超参数优化算法和3个模型选择标准.
主要成果:
- 模型选择对于有效的系统识别至关重要.
- 用树结构帕森估计器 (TPE) 的贝叶斯优化对于平衡的模型选择是有效的.
- K-fold交叉验证是一种强大的绩效评估指标;对于大型数据集,信息标准是有效的.
- 具有平衡复杂性的ML模型,例如树集,表现出卓越的预测准确性和计算效率.
结论:
- 以MLOps为灵感的工作流可以增强PSE中的系统识别.
- 建议使用平衡复杂度的ML模型,以在PSE中获得卓越的性能.
- 结果为PSE从业人员在模型选择和评估中提供了可操作的见解.
相关概念视频
01:26Classification of Systems-I
167
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
167
01:17Open and closed-loop control systems
601
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
601
01:26Feedback control systems
268
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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01:31Classification of Systems-II
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
133
01:10Control Systems
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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
At the heart...
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01:21Linear Approximation in Time Domain
59
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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