相关实验视频

Updated: Sep 13, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

在线软传感器建模的增量多级学习MLP模型

Yihan Wang¹, Jiahao Tao², Liang Zhao²

¹College of Artificial Intelligence, Beijing Normal University, Beijing 100875, China.

Sensors (Basel, Switzerland)

|July 30, 2025

概括

本研究引入了一种增量多变量多步预测多层感知回归软传感模型 (MVMS-MLP),以改进实时工业过程监控. 这种新的方法提高了动态条件下的适应性和准确性,克服了传统软传感器的局限性.

科学领域:

化学工程是化学工程的重要组成部分.
数据科学数据科学数据科学
过程控制过程控制

背景情况:

工业生产面临着时间变化条件和连续时间序列数据的挑战.
传统的软传感器模型与动态变化作斗争,导致性能低于最佳.
在线分析系统是昂贵的,有维护问题,并遭受测量延迟,阻碍实时控制.

研究的目的:

开发一个适应性和准确的软传感模型,用于工业过程.
在动态和时间变化的环境中解决传统软传感器的局限性.
通过改进的预测能力,实现实时监控和控制.

主要方法:

引入一个多变量多步预测多层感知回归软传感模型 (MVMS-MLP).
整合增量学习策略,以提高适应性和准确性.
开发一个预训练的MVMS-MLP模型,包括时间数据处理和MLP回归,然后进行增量模型构建.

主要成果:

增量MVMS-MLP模型显示了对工业过程动态变化的更强的适应性.
与传统方法相比,该模型在多变量预测中获得了更高的准确性.
通过基准问题和现实世界的工业案例研究来验证有效性.

结论:

拟议的增量MVMS-MLP为复杂工业环境中的实时软传感提供了强大的解决方案.
增量学习显著提高了多变量软传感器的性能和适应性.
该方法为过程控制的昂贵和延迟的在线分析系统提供了可行的替代方案.

关键词:

在MVPMS-MLP中使用.增量学习是一种增量学习.工业过程工业过程工业过程软传感感知模型的模型

更多相关视频

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

相关概念视频

Steps in the Modeling Process

Steps in the Modeling Process

Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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...

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...