Genetic Programming for the Identification of Nonlinear Input−Output Models

Madár, János; Abonyi, János; Szeifert, Ferenc

doi:10.1021/ie049626e

Cited by 163 publications

(104 citation statements)

References 25 publications

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“…O controlador se demonstrou robusto, sendo que com as válvulas mais desgastadas o sistema ficou um pouco mais lento. De acordo com [19], os modelos das 3 válvulas foram obtidos através da aplicação de uma ferramenta de identificação proposta por Madár, Abonyi e Szeifert [20], a qual utiliza Programação Genética e o algoritmo dos Mínimos Quadrados Ortogonais. Tais modelos são descritos nas equações a seguir, respectivamente para as válvulas 1, 2 e 3, onde u(k) é a entrada e y(k) é a saída do modelo no instante k: …”

Section: Resultsunclassified

C-GRASP Para Otimização De Um Controlador PI Com Anti-Windup Aplicado A Uma Válvula De Recirculação De Gases De Escape

Neto¹,

Coelho²

2016

Anais Do 10. Congresso Brasileiro De Inteligência Computacional

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Section: Resultsunclassified

C-GRASP Para Otimização De Um Controlador PI Com Anti-Windup Aplicado A Uma Válvula De Recirculação De Gases De Escape

Neto¹,

Coelho²

2016

Anais Do 10. Congresso Brasileiro De Inteligência Computacional

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“…The proposed NON-SC-FR was implemented based on the routines of GP Matlab package [40] which is available for the public. The following GP parameters [40], which are effective for developing polynomial models, were used: population size = 50; maximum number of generations = 500; crossover rate = 0.5; mutation rate = 0.5.…”

Section: Evaluations Of Algorithmic Effectivenessmentioning

confidence: 99%

“…The following GP parameters [40], which are effective for developing polynomial models, were used: population size = 50; maximum number of generations = 500; crossover rate = 0.5; mutation rate = 0.5.…”

Section: Evaluations Of Algorithmic Effectivenessmentioning

confidence: 99%

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Chan

Lam

Yiu

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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-Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming (GP) to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account.

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“…Equation (15) aims at fitting training data sets to the fuzzy regression model, and it avoids generating fuzzy regression models with too many insignificant terms. It is designed to find a balance between minimizing the number of terms and maximizing model accuracy, since a fuzzy regression model which contains many insignificant terms reduces its interpretation (Madar et al 2005). …”

Section: Fitness Functionmentioning

confidence: 99%

“…The parameters used in the intelligent fuzzy regression were set as shown in Table 3 with reference to (Madar et al 2005). Since intelligent fuzzy regression is a stochastic method, different results will be obtained from different runs.…”

Section: Model Developmentmentioning

confidence: 99%

An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness

Chan

Kwong

Dillon

et al. 2011

Journal of Engineering Design

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Affective product design aims at incorporating customers' affective needs into design variables of a new product so as to optimize customers' affective satisfaction. Faced with fierce competition in marketplaces, companies try to determine the settings in order to maximize customers' affective satisfaction with products. To achieve this, a set of customer survey data is required in order to develop a model which relates customers' affective responses to the design variables of a new product. Customer survey data is usually fuzzy since human feeling is usually fuzzy, and the relationship between customers' affective responses and design variables is usually nonlinear. However, previous research on modelling the relationship between affective response and design variables has not addressed the development of explicit models involving either nonlinearity or fuzziness. In this paper, an intelligent fuzzy regression approach is proposed to generate models which represent this nonlinear and fuzzy relationship between affective responses and design variables. In order to do this,  K.Y. Chan is the corresponding author. His email address is Kit.Chan@curtin.edu.au.we extend the existing work on fuzzy regression by first utilizing an evolutionary algorithm to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. The fuzzy regression algorithm is then used to determine the fuzzy coefficients of the model. The models thus developed are explicit, and consist of fuzzy, nonlinear terms which relate affective responses to design variables. A case study of affective product design of mobile phones is used to illustrate the proposed method. h refers to the degree to which the fuzzy linear model fits the data sets in developing the fuzzy model. Keywords: Fuzzy regression, evolutionary algorithm, affective product design

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Genetic Programming for the Identification of Nonlinear Input−Output Models

Cited by 163 publications

References 25 publications

C-GRASP Para Otimização De Um Controlador PI Com Anti-Windup Aplicado A Uma Válvula De Recirculação De Gases De Escape

C-GRASP Para Otimização De Um Controlador PI Com Anti-Windup Aplicado A Uma Válvula De Recirculação De Gases De Escape

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness

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