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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
96
Linear time-invariant Systems01:23

Linear time-invariant Systems

289
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
289
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

101
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,...
101
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

129
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
129
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

182
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
182
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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多层在线序列缩小内核极端学习基于机器建模的时间变化的分布式参数系统.

Chengjiu Zhu, Haidong Yang, Xi Jin

    IEEE transactions on cybernetics
    |August 1, 2023
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    概括
    此摘要是机器生成的。

    本研究介绍了一种新型的多层在线序列缩小内核极端学习机器 (ML-OSRKELM),用于模拟复杂的工业过程. 该方法准确地捕捉了分布式参数系统 (DPS) 中的时间变化的动态和非线性.

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    科学领域:

    • 工程 工程师 工程师 工程师
    • 机器学习 机器学习
    • 控制系统 控制系统

    背景情况:

    • 工业动态过程往往表现出时间变化和非线性特征.
    • 分布式参数系统 (DPS) 的准确建模对于控制和优化至关重要.
    • 现有的方法可能很难有效地捕捉空间和时间动态.

    研究的目的:

    • 为时间变化的DPS开发一个在线时空建模方法.
    • 解决工业过程中非线性和时间变化的行为所带来的挑战.
    • 为高效的DPS建模提出一个深度学习框架.

    主要方法:

    • 开发了一个多层在线序列减少内核极端学习机器 (ML-OSRKELM).
    • 该方法使用堆叠的在线顺序减少内核极端学习机器自编码器 (OSRKELM-AEs) 来减少维度.
    • 在线序列缩小内核极端学习机器 (OS-RKELM) 用于时间建模和时空重建.

    主要成果:

    • ML-OSRKELM有效地将时空数据翻译成一个低维时间域.
    • 内核技巧和支持向量选择优化非线性学习并减少冗余信息.
    • 顺序更新方案允许实时进行参数调整.

    结论:

    • 拟议的ML-OSRKELM方法为时间变化的DPS提供了准确和高效的在线时空建模.
    • 离子电池的热过程的实验验证证明了该模型的出色性能.
    • 这种方法为捕捉现实世界工业应用中的复杂动态提供了有前途的解决方案.