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基于ISOA-GPR权重合奏学习的海洋溶酶发酵过程的软传感器建模方法.

Na Lu¹, Bo Wang¹, Xianglin Zhu¹

¹Key Laboratory of Agricultural Measurement and Control Technology and Equipment for Mechanical Industrial Facilities, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.

Sensors (Basel, Switzerland)

|November 25, 2023

概括

这项研究引入了一种改进的软传感器模型,用于海洋酶发酵,通过使用合体学习和高斯过程回归 (GPR) 来提高关键生物化学参数的预测准确性. 该模型有效地处理具有有限数据的非线性系统.

关键词:

高斯过程回归的高斯过程回归.灰度的相关性分析分析.海洋溶解酶是一种海洋溶解酶.海优化算法海优化算法软传感器是一种软传感器.

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科学领域:

生物化学工程生物化学工程
过程控制过程控制
机器学习机器学习

背景情况:

海洋溶酶发酵表现出复杂的非线性,多阶段和时间变化的动态.
传统的单软传感器模型很难有效地捕捉这些动态特征.

研究的目的:

开发一种新的加权集体学习软传感器建模方法,用于海洋酶发酵.
提高发酵过程中关键生化参数的预测准确度.

主要方法:

使用改进的密度峰集群算法 (ADPC) 来进行数据子集划分.
采用改进的海优化算法 (ISOA) 来优化高斯过程回归 (GPR) 模型,创建子预测模型.
开发了一个基于样本连接的融合策略,用于模型集成.

主要成果:

拟议的软传感器模型准确地预测了海洋酶发酵中的关键生物化学参数.
即使使用有限的训练数据,也表现出有效的性能,显示出相对较小的预测错误.
验证了模型在处理非线性系统方面的能力.

结论:

权重组合学习软传感器模型为预测发酵参数提供了强大的解决方案.
该方法显示了在一般非线性系统的软传感器预测中更广泛应用的潜力.
这种方法增强了生物化学工程中的过程监测和控制.