Optimization of fuzzy model using genetic algorithm for process control application

Yusof, Rubiyah; Rahman, Ribhan Zafira Abdul; Khalid, Marzuki; Ibrahim, Mohd Faisal

doi:10.1016/j.jfranklin.2010.10.004

Cited by 34 publications

(11 citation statements)

References 19 publications

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“…Then, the MOHGA and the recursive least squares estimator (RLSE) are used to obtain the optimized fuzzy models. A fuzzy modeling approach for the identification of nonlinear control processes was discussed in Yusof et al (2011). This approach is based on a combination of genetic algorithm and recursive least squares.…”

Section: Related Workmentioning

confidence: 99%

Approximation of two-variable functions using high-order Takagi–Sugeno fuzzy systems, sparse regressions, and metaheuristic optimization

Wiktorowicz

Krzeszowski

2020

Soft Comput

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This paper proposes a new hybrid method for training high-order Takagi–Sugeno fuzzy systems using sparse regressions and metaheuristic optimization. The fuzzy system is considered with Gaussian fuzzy sets in the antecedents and high-order polynomials in the consequents of fuzzy rules. The fuzzy sets can be chosen manually or determined by a metaheuristic optimization method (particle swarm optimization, genetic algorithm or simulated annealing), while the polynomials are obtained using ordinary least squares, ridge regression or sparse regressions (forward selection, least angle regression, least absolute shrinkage and selection operator, and elastic net regression). A quality criterion is proposed that expresses a compromise between the prediction ability of the fuzzy model and its sparsity. The conducted experiments showed that: (a) the use of sparse regressions and/or metaheuristic optimization can reduce the validation error compared with the reference method, and (b) the use of sparse regressions may simplify the fuzzy model by zeroing some of the coefficients.

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Section: Related Workmentioning

confidence: 99%

Approximation of two-variable functions using high-order Takagi–Sugeno fuzzy systems, sparse regressions, and metaheuristic optimization

Wiktorowicz

Krzeszowski

2020

Soft Comput

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“…Fuzzy logic control has become increasingly popular in recent years [26]. Its ability to control imprecise or vague processes has been used to perform the control of numerous industrial applications [27].…”

Section: Software Implementationmentioning

confidence: 99%

Cooperative controllers for highways based on human experience

Pérez

Milanés

Godoy

et al. 2013

Expert Systems with Applications

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The AUTOPIA program has been working on the development of intelligent autonomous vehicles for the last ten years. Its latest advances have focused on the development of cooperative manoeuvres based on communications involving several vehicles. However, so far, these manoeuvres have been tested only on private tracks that emulate urban environments. The first experiments with autonomous vehicles on real highways, in the framework of the Grand Cooperative Driving Challenge (GCDC) where several vehicles had to cooperate in order to perform Cooperative Adaptive Cruise Control (CACC), are described. In this context, the main challenge was to translate, through fuzzy controllers, human driver experience to these scenarios. This communication describes the experiences deriving from this competition, specifically that concerning the controller and the system implemented in a Citröen C3.

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“…[14][15][16] Also, in modeling researches, it is used to optimize the ANN and FL model parameters. [17][18][19] Despite the significant role of FT on PVC properties, the studies reported in declaring or characterizing effects of material and processing variables are few. Specially, no modeling research is reported except the article published by the authors.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic model for prediction of fusion characteristics in rigid polyvinyl chloride nanocomposite

Tavana

Moghri

Esmaili

2016

Journal of Elastomers & Plastics

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Fusion is a key parameter in achieving polyvinyl chloride (PVC) nanocomposites with desired properties. In the present research, a fuzzy logic (FL)-based model is developed to predict fusion time (FT) for the contents of nanoclay, processing aid, and calcium stearate in PVC processing. In order to have precise rules for the FL model, data mining algorithm RepTree is employed to detect dominating patterns among experimental data. The model parameters are then well adjusted using genetic algorithm. The modeling results show a correlation of 0.86 between predicted and observed values for FT. So, it proved reliability of the idea of employing the decision tree resulted from a data mining algorithm as the base knowledge of FL models. Also, applying genetic algorithm optimization, the correlation coefficient increased from a value of less than 0.83 to 0.86. The calculated correlation coefficient for the test data was 0.88, which denotes good model universalizing ability.

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Optimization of fuzzy model using genetic algorithm for process control application

Cited by 34 publications

References 19 publications

Approximation of two-variable functions using high-order Takagi–Sugeno fuzzy systems, sparse regressions, and metaheuristic optimization

Approximation of two-variable functions using high-order Takagi–Sugeno fuzzy systems, sparse regressions, and metaheuristic optimization

Cooperative controllers for highways based on human experience

Fuzzy logic model for prediction of fusion characteristics in rigid polyvinyl chloride nanocomposite

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