Multi-Model Modelling and Predictive Control Based on Local Model Networks

Zhi, Xue; Li, S. Y.

doi:10.2316/journal.201.2006.2.201-1554

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…These always lead to a repetitive trial of different number of clusters for a satisfying result, which is attributed for poor computation efficiency. To avoid the earlier problem, an SFCM algorithm is applied in this paper [28]. The algorithm integrating FCM into LMN identification is briefed hereafter.…”

Section: B Lmn Identification Based On Sfcmmentioning

confidence: 99%

Electricity Gain via Integrated Operation of Turbine Generator and Cooling Tower Using Local Model Network

Pan

Shieh

Jang

et al. 2011

IEEE Trans. Energy Convers.

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This paper presents a model-based optimization approach to determine operation mode of a set of fans in the cooling tower (CT) by considering turbine generator and CT as an integrated system. The optimization problem is formulated as to maximize the net power output, which is the result of subtracting power consumption of fans in CT from power generated by turbine generator. The operations of both units are characterized by a data-driven method, local model network, which presents a set of local models in the formats of linear equations. Stepwise variable selection is employed to identify key variables as a way of reducing the number of variables and avoiding overfitting. Satisfactory fuzzy c-mean cluster is used to categorize operation data into several groups to build local models. The optimal problem is then solved by linear programming algorithm. A case study conducted in a commercial plant demonstrates that the proposed approach can increase significant net power output.

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Section: B Lmn Identification Based On Sfcmmentioning

confidence: 99%

Electricity Gain via Integrated Operation of Turbine Generator and Cooling Tower Using Local Model Network

Pan

Shieh

Jang

et al. 2011

IEEE Trans. Energy Convers.

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“…In order to describe a discrete nonlinear system, Eqs. (1) and (2) can also be functionally represented in discrete form as…”

Section: Takagi-sugeno (T-s) Fuzzy Modelmentioning

confidence: 99%

“…The multiple-linear models concept has been used in the recent years for modeling of nonlinear systems [1]. In addition, multiple-linear model based approaches for controller design [2][3][4][5] have attracted the process control community. A plethora of multiple-model adaptive control schemes have been proposed in the control literature [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor

Prakash

Srinivasan

2009

ISA Transactions

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“…[16,17]. In short, whereas many architectures using multiple models and neural networks have been proposed, there has not been much work on clustering techniques, based on neural networks and K-means algorithms [18], applied to traditional multimodel representation using only input/output data. The most tedious issues are related to the model-base size and the clustering procedure which aims to the determination of the operating domains.…”

Section: Introductionmentioning

confidence: 99%

A Multimodel Approach for Complex Systems Modeling based on Classification Algorithms

Elfelly

Dieulot

Benrejeb

et al. 2014

INT J COMPUT COMMUN

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In this paper, a new multimodel approach for complex systems modeling based on classification algorithms is presented. It requires firstly the determination of the model-base. For this, the number of models is selected via a neural network and a rival penalized competitive learning (RPCL), and the operating clusters are identified by using the fuzzy K-means algorithm. The obtained results are then exploited for the parametric identification of the models. The second step consists in validating the proposed model-base by using the adequate method of validity computation. Two examples are presented in this paper which show the efficiency of the proposed approach.

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Multi-Model Modelling and Predictive Control Based on Local Model Networks

Cited by 12 publications

References 15 publications

Electricity Gain via Integrated Operation of Turbine Generator and Cooling Tower Using Local Model Network

Electricity Gain via Integrated Operation of Turbine Generator and Cooling Tower Using Local Model Network

Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor

A Multimodel Approach for Complex Systems Modeling based on Classification Algorithms

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