Soft-sensing modeling of marine protease fermentation process based on improved PSO-RBFNN

ZHU Xianglin; LING Jing; WANG Bo; HAO Jianhua; DING Yuhan

doi:10.11949/j.issn.0438-1157.20170598

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Soft-sensing modeling of marine protease fermentation process based on improved PSO-RBFNN

更新时间：2025-12-10

- Soft-sensing modeling of marine protease fermentation process based on improved PSO-RBFNN
- CIESC Journal Issue 3, Pages: 1221-1227(2018)
- 作者机构：
  
  1. 江苏大学电气信息工程学院,江苏,镇江,212013
  2. 中国水产科学研究院黄海水产研究所, 海洋酶与酶工程开放实验室,山东,青岛,266071
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20170598
  CLC： P301
- Online First：05 March 2018，
  
  Published：05 March 2018
- 稿件说明：
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ZHU Xianglin, LING Jing, WANG Bo, et al. Soft-sensing modeling of marine protease fermentation process based on improved PSO-RBFNN[J]. CIESC Journal, 2018, (3): 1221-1227.
DOI：

ZHU Xianglin, LING Jing, WANG Bo, et al. Soft-sensing modeling of marine protease fermentation process based on improved PSO-RBFNN[J]. CIESC Journal, 2018, (3): 1221-1227. DOI： 10.11949/j.issn.0438-1157.20170598.

摘要

针对海洋蛋白酶（marine protease，MP）发酵过程中某些关键参量难以在线检测，离线测量存在大滞后、易染菌的问题，提出了一种基于改进的粒子群-径向基神经网络（PSO-RBFNN）的MP发酵过程软测量建模方法。首先采用指数下降惯性权重（exponential decreasing inertia weight，EDIW）策略对粒子群算法进行改进，克服了固定惯性权重和自适应惯性权重的粒子群算法易于陷入局部极小，进化后期收敛速度慢以及全局搜索能力弱的缺点；然后，采用改进后的粒子群算法对径向基神经网络连接权值进行在线优化，确定RBFNN拓扑结构；最后，根据MP发酵过程的输入/输出向量构建RBFNN软测量模型。实验仿真结果表明，EDIW策略改进的PSO-RBFNN软测量模型训练时间缩短了40%左右，模型预测精度提高了3%以上。

Abstract

Some key parameters in the fermentation process of marine protease (MP) are difficult to be detected online. There is the existence of large time-delay and easily stained bacteria in off-line measurement. A soft sensor modeling method based on the improved PSO-RBFNN in the MP fermentation process was proposed. Firstly

exponential decreasing inertia weight (EDIW) strategy was used to improve PSO algorithm

and overcome the disadvantages that PSO with fixed inertia weight and adaptive inertia weight is easy to fall into the local minimum

the convergence rate is slow in late evolution and the global search ability is weak. Then the improved PSO algorithm was used to optimize the connection weight of RBFNN

and the RBFNN topology was successively determined. Finally

the RBFNN soft sensor model was constructed according to the input/output vector of MP fermentation process. The simulation results showed that the training time of the EDIW-PSO-RBFNN model was reduced by at least (about) 40%

and the prediction accuracy of model was improved by more than 3%.

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