Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers

Abstract

a b s t r a c tFuzzy regression (FR) been demonstrated as a promising technique for modeling manufacturing processes where availability of data is limited. FR can only yield linear type FR models which have a higher degree of fuzziness, but FR ignores higher order or interaction terms and the influence of outliers, all of which usually exist in the manufacturing process data. Genetic programming (GP), on the other hand, can be used to generate models with higher order and interaction terms but it cannot address the fuzziness of the manufacturing process data. In this paper, genetic programming-based fuzzy regression (GP-FR), which combines the advantages of the two approaches to overcome the deficiencies of the commonly used existing modeling methods, is proposed in order to model manufacturing processes. GP-FR uses GP to generate model structures based on tree representation which can represent interaction and higher order terms of models, and it uses an FR generator based on fuzzy regression to determine outliers in experimental data sets. It determines the contribution and fuzziness of each term in the model by using experimental data excluding the outliers. To evaluate the effectiveness of GP-FR in modeling manufacturing processes, it was used to model a non-linear system and an epoxy dispensing process. The results were compared with those based on two commonly used FR methods, Tanka's FR and Peters' FR. The prediction accuracy of the models developed based on GP-FR was shown to be better than that of models based on the other two FR methods.

Prediction of the Unified Parkinson’s Disease Rating Scale assessment using a genetic programming system with geometric semantic genetic operators

“…In the last few years, GP has been extensively used both in Industry and Academia (Arcuri & Yao, 2010;Chan, Kwong, & Fogarty, 2010;Choi & Choi, 2012;dos Santos, Ferreira, Torres, Gonçalves, & Lamparelli, 2011;Koza, Streeter, & Keane, 2008;Moreno-Torres, Llorá, Goldberg, & Bhargava, 2013;Ravisankar, Ravi, & Bose, 2010;Trujillo, Legrand, Olague, & Lévy-Véhel, 2012;Yeun, Suh, & Yang, 2000;Wongseree, Chaiyaratana, Vichittumaros, Winichagoon, & Fucharoen, 2007) and it has produced a wide set of results that have been defined human-competitive (Koza, 2010). While these results have demonstrated the appropriateness of GP in tackling real-life problems, research has recently focused on developing new variants of GP in order to further improve its performance.…”

Robust fuzzy regression analysis

“…If the median of the estimated squared residuals at the kth iteration is lower than the one obtained at the (k À 1)th iteration, 4 we keep a à k ; b à k ; d à k ; g à k and h à k as optimal parameter estimates. In order to enhance these estimates, we employ the WLS procedure, assigning to each observation a weight.…”

Learning approaches for developing successful seller strategies in dynamic supply chain management

“…Many related works in the late 1990s still concentrated only on solving tasks in separate subareas [16,30], omitting a number of key parameters and constraints, such as temporal and capacity constraints [1,2,52]. It is only at the turn of the century when researchers raised the issue of the dynamic nature of the SCM problem domain [50], and started to look at the SCM as an integrated process that faces multiple interrelated constraints [49] and uncertainty [12,39,54].…”