scholarly journals Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Weili Xiong ◽  
Wei Zhang ◽  
Dengfeng Liu ◽  
Baoguo Xu
Keyword(s):  
Fermentation Process ◽  
Soft Sensor ◽  
Least Square ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Fermentation Processes ◽  
Numerical Examples ◽  
Effectiveness And Efficiency ◽  
Sensor Modeling ◽  
Penicillin Fermentation

Due to the complexity and uncertainty of microbial fermentation processes, data coming from the plants often contain some outliers. However, these data may be treated as the normal support vectors, which always deteriorate the performance of soft sensor modeling. Since the outliers also contaminate the correlation structure of the least square support vector machine (LS-SVM), the fuzzy pruning method is provided to deal with the problem. Furthermore, by assigning different fuzzy membership scores to data samples, the sensitivity of the model to the outliers can be reduced greatly. The effectiveness and efficiency of the proposed approach are demonstrated through two numerical examples as well as a simulator case of penicillin fermentation process.

Intelligent Control System Based on SVM Combined with Soft Sensor for Microbiological Fermentation

2010 ◽  
Vol 20-23 ◽  
pp. 1185-1191
Author(s):  
Jin Hai Wu ◽  
Tao Cen
Keyword(s):  
Variable Selection ◽  
Fermentation Process ◽  
Information Criterion ◽  
Soft Sensor ◽  
Support Vector ◽  
Intelligent Control System ◽  
Two Factors ◽  
Novel Method ◽  
Input Variables ◽  
Sensor Modeling

The accuracy of SVM in fermentation process is mainly impacted by two factors input variable selection and parameter setting in SVM training procedures. In this paper, a novel method is proposed to solve the problem． The selection problem of SVM parameters and input variables is considered as a compound optimization problem. A new compound optimal objective function based on Akaike information criterion is constructed. In this paper, we propose a new method of soft sensor constructed with generalized support vector machine for microbiological fermentation. Experiment results demonstrate this method is an effective approach for parameter selection and input variable selection and has good performance for soft sensor modeling in microorganism fermentation process.

scholarly journals Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhu Li ◽  
Khalil Ur Rehman ◽  
Liu Wenhui ◽  
Faiza Atique
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Fermentation Process ◽  
Soft Sensor ◽  
Projection Algorithm ◽  
Critical Parameters ◽  
Support Vector ◽  
Sensor Model ◽  
Factor C ◽  
Sensor Modeling

The marine protease fermentation process is a highly nonlinear, time-varying, multivariable, and strongly coupled complex biochemical reaction process. Due to the growth and reproduction of living organisms, the internal mechanism is very complicated. Some key variables (such as cell concentration, substrate concentration, and enzyme activity) that directly reflect the fermentation process's quality are difficult to measure in real-time by traditional measurement methods. A soft sensor model based on a support vector regression (SVR) is proposed in this paper to resolve this problem. To further improve the model's prediction accuracy, the grey wolf optimization (GWO) algorithm is used to optimize the critical parameters (kernel function width σ, penalty factor c, and insensitivity coefficient ε) of the SVR model. To study the influence of selecting auxiliary variables on soft sensor modeling, the successive projection algorithm (SPA) is used to determine the characteristic variables and compare them with grey relation analysis (GRA) algorithm. Finally, the Excel spreadsheet data was called by MATLAB programming, and the established SPA-GWO-SVR soft sensor model predicted crucial biological variables. The simulation results show that the SPA-GWO-SVR model has higher prediction accuracy and generalization ability than the traditional SPA-SVR model. The real-time monitoring was processed by MATLAB software for the marine protease fermentation process, which met the requirements of optimal control of the marine protease fermentation process.

scholarly journals Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7635
Author(s):  
Bo Wang ◽  
Xingyu Wang ◽  
Mengyi He ◽  
Xianglin Zhu
Keyword(s):  
Pichia Pastoris ◽  
Least Squares ◽  
Fermentation Process ◽  
Model Simulation ◽  
Weighted Least Squares ◽  
Soft Sensor ◽  
Modeling Method ◽  
Local Model ◽  
Support Vector ◽  
Sensor Modeling

The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.

scholarly journals The use of entropy based fuzzy membership on weighted logistic regression for the unbalanced data

2021 ◽  
Vol 880 (1) ◽  
pp. 012048
Author(s):  
Ajiwasesa Harumeka ◽  
Santi Wulan Purnami ◽  
Santi Puteri Rahayu
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Rare Event ◽  
Least Square ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Unbalanced Data ◽  
Fuzzy Support Vector Machine ◽  
Minority Class ◽  
Weighted Logistic Regression

Abstract Logistic regression is a popular and powerful classification method. The addition of ridge regularization and optimization using a combination of linear conjugate gradients and IRLS, called Truncated Regularized Iteratively Re-weighted Least Square (TR-IRLS), can outperform Support Vector Machine (SVM) in terms of processing speed, especially when applied to large data and have competitive accuracy. However, neither SVM nor TR-IRLS is good enough when applied to unbalanced data. Fuzzy Support Vector Machine (FSVM) is an SVM development for unbalanced data that adds fuzzy membership to each observation. The fuzzy membership makes the interest of each observation in the minority class higher than the majority class. Meanwhile, TR-IRLS developed into a Rare Event Weighted Logistic Regression (RE-WLR) by adding weight to logistic regression and bias correction. The weighting of the RE-WLR depends on the undersampling scheme. It allows an “information loss”. Between FSVM and RE-WLR has a similarity, the weight based only on class differences (minority or majority). Entropy Based Fuzzy Support Vector Machine (EFSVM) is a method used to accommodate the weaknesses of FSVM by considering the class certainty of class observations. As a result, EFSVM is able to improve SVM performance for unbalanced data, even beating FSVM. For this reason, we use EF on the TR-IRLS algorithm to classify large and unbalanced data, as a proposed method. This method is called Entropy-Based Fuzzy Weighted Logistic Regression (EF-WLR). This Research shows the review of EF-WLR for unbalanced data classification.

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