Estimation of nonlinear systems using linear multiple models

Banerjee, Atanu; Arkun, Yaman; Ogunnaike, B. A.; Pearson, Ronald K.

doi:10.1002/aic.690430511

Cited by 111 publications

(63 citation statements)

References 16 publications

(13 reference statements)

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“…These variables and their units are given in Table 1. Banerjee et al (1997) the solvent feed rate G s and the reactor feed temperature T rf are treated as measured disturbance variables, and are used along with the other simulation inputs for state estimation. The inhibitor flowrate is treated as an unmeasured disturbance, and is not used for estimation purposes.…”

Section: Solution Copolymerization Simulationmentioning

confidence: 99%

RBFN Identification of an Industrial Polymerization Reactor Model

Bomberger¹,

Seborg²

2001

Application of Neural Networks and Other Learning Technologies in Process Engineering

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Methods developed for radial basis function network (RBFN) identification are applied to a complex multiple-input, multiple-output (MIMO) simulation of a solution copolymerization reactor. For RBFN identification, k-means clustering and stepwise regression analysis are used. The practicality of applying these methods to large industrial identification problems is discussed, considering the restrictions of industrially practical input sequence design. The RBFN model has three inputs and two outputs, and the dimensionality of the identification problem poses some difficulties for nonlinear empirical model identification; specifically, the large amount of data required is a problem for plant testing and may cause computational difficulties for identification algorithms as well.

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Section: Solution Copolymerization Simulationmentioning

confidence: 99%

RBFN Identification of an Industrial Polymerization Reactor Model

Bomberger¹,

Seborg²

2001

Application of Neural Networks and Other Learning Technologies in Process Engineering

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“…This algorithm can be used in the same manner as the classical multiple model algorithms for other applications. Examples of such applications are, but not limited to, multiple model adaptive control (Athans et al, 2005), fault-tolerant control (Zhang and Jiang, 2001) and approximation of nonlinear systems with multiple models (Banerjee et al, 1997). This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Model Weight and State Estimation for Multiple Model Systems Applied to Fault Detection and Identification

Hallouzi

Verhaegen

Babuška

et al. 2006

IFAC Proceedings Volumes

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“…The index values are always a certain number within this interval, and there will be preference information, which may result in error in decision making [1]. In References [2][3], the expression of index numerical number using interval number is proposed. However, the interval value will be too big in such a case.…”

Section: Introductionmentioning

confidence: 99%

Method for Multi-attribute Decision Making with Triangular Fuzzy Number Based on Multi-period State

Sha¹

2015

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This paper takes the time weight and attribute weight in different periods into consideration to propose a dynamic triangular fuzzy number type multi-attribute decision making method to solve the problem with multi-attribute decision making with triangular fuzzy number as the attribute value. This method utilizes the characteristics of the triangular fuzzy number in order to establish the correlation model between the evaluation scheme and the positive and negative ideal scheme, and obtain comprehensive ranking of the evaluation scheme, thus acquiring the decision making result. At last, this paper demonstrates the feasibility and validity of the proposed methods through instance analysis.

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Estimation of nonlinear systems using linear multiple models

Cited by 111 publications

References 16 publications

RBFN Identification of an Industrial Polymerization Reactor Model

RBFN Identification of an Industrial Polymerization Reactor Model

Model Weight and State Estimation for Multiple Model Systems Applied to Fault Detection and Identification

Method for Multi-attribute Decision Making with Triangular Fuzzy Number Based on Multi-period State

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