PI adaptive LS-SVR control scheme with disturbance rejection for a class of uncertain nonlinear systems

Almasi, Omid Naghash; Khooban, Mohammad Hassan

doi:10.1016/j.engappai.2015.11.009

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…Since models with less training time are preferred, this issue prejudices users against using or selecting SVR. Next, a kernel function is selected based on experience, , and different kernel functions are rarely utilized. The inputs of a real-world problem like MSMPR have different levels of importance .…”

Section: Introductionmentioning

confidence: 99%

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Inapakurthi

Naik

Mitra

2022

Ind. Eng. Chem. Res.

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Mixed-suspension mixed-product removal (MSMPR) crystallization process is critical for optimal separation and purification operations in pharmaceutical and fine chemical industries. To achieve this, detailed mathematical model-based optimization is the current practice, which is reported as an extremely time-consuming exercise, prohibiting their online implementation. To facilitate faster optimization, a novel datadriven modeling and optimization algorithm has been proposed in this work. Limited number of high-fidelity data from the computationally expensive model were utilized to build surrogates using support vector regression (SVR). Inputs of MSMPR are assigned different, instead of same, kernel parameters, and multiple kernels were explored to capture the complex dynamics of the crystallization process. Loaded with a sample size estimation algorithm to reduce the computational load on model execution time, the complete formulation is solved using an evolutionary algorithm enabling evolution of optimal SVR-based surrogate models. From the Pareto optimal set of several such models, two instances with differing prediction error were selected and the optimization of MSMPR was performed. The study indicates 3 orders of magnitude faster optimization with the surrogate models and 87.54% savings in the number of expensive function evaluations compared to the physics-based MSMPR model facilitating online optimization of such a process.

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Section: Introductionmentioning

confidence: 99%

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Inapakurthi

Naik

Mitra

2022

Ind. Eng. Chem. Res.

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“…is the fitting function [15]. Thus, the solution of the optimal linear function for SVR is expressed as the following constraint optimization problem:…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and Its Application

Fan

Yan

2017

Mathematical Problems in Engineering

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In modeling, only information from the deviation between the output of the support vector regression (SVR) model and the training sample is considered, whereas the other prior information of the training sample, such as probability distribution information, is ignored. Probabilistic distribution information describes the overall distribution of sample data in a training sample that contains different degrees of noise and potential outliers, as well as helping develop a high-accuracy model. To mine and use the probability distribution information of a training sample, a new support vector regression model that incorporates probability distribution information weight SVR (PDISVR) is proposed. In the PDISVR model, the probability distribution of each sample is considered as the weight and is then introduced into the error coefficient and slack variables of SVR. Thus, the deviation and probability distribution information of the training sample are both used in the PDISVR model to eliminate the influence of noise and outliers in the training sample and to improve predictive performance. Furthermore, examples with different degrees of noise were employed to demonstrate the performance of PDISVR, which was then compared with those of three SVR-based methods. The results showed that PDISVR performs better than the three other methods.

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Stochastic stabilization problem of complex networks without strong connectedness

Liu

Mei

2018

Applied Mathematics and Computation

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PI adaptive LS-SVR control scheme with disturbance rejection for a class of uncertain nonlinear systems

Cited by 8 publications

References 33 publications

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and Its Application

Stochastic stabilization problem of complex networks without strong connectedness

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