Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process

A new strategy for internal model control (IMC) is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM). Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is const...

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Vydané v:Mathematical problems in engineering Ročník 2015; číslo 2015; s. 1 - 9
Hlavní autori: Li, Dazi, Jin, Qibing, Xie, Qianwen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015
John Wiley & Sons, Inc
Predmet:
Algorithms
Approximation
Artificial neural networks
Coefficients
Convergence
Extreme values
Identification
Inverse
Learning
Machine learning
Mathematical models
Neural networks
Nonlinear control
Nonlinearity
Parameter estimation
Parameter identification
Regression models
Strategy
Support vector machines
Beijing China
China
ISSN:1024-123X, 1563-5147
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Shrnutí:A new strategy for internal model control (IMC) is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM). Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS), while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.
Bibliografia:ObjectType-Article-1
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ISSN:1024-123X
1563-5147
DOI:10.1155/2015/181389