Possibilistic linear systems and their application to the linear regression model
Fuzzy Sets and Systems
Evaluation of fuzzy linear regression models
Fuzzy Sets and Systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
A fuzzy approach for multiresponse optimization: an off-line quality engineering problem
Fuzzy Sets and Systems
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Further examination of fuzzy linear regression
Fuzzy Sets and Systems
Fuzzy regression methods—a comparative assessment
Fuzzy Sets and Systems
Outliers detection and confidence interval modification in fuzzy regression
Fuzzy Sets and Systems
A fuzzy logic-based computational recognition-primed decision model
Information Sciences: an International Journal
Fuzzy functions with support vector machines
Information Sciences: an International Journal
Asymptotic properties of least squares estimation with fuzzy observations
Information Sciences: an International Journal
Computers and Electronics in Agriculture
Forward and reverse modeling in MIG welding process using fuzzy logic-based approaches
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Information Sciences: an International Journal
Information Sciences: an International Journal
Routine high-return human-competitive automated problem-solving by means of genetic programming
Information Sciences: an International Journal
Inference of differential equation models by genetic programming
Information Sciences: an International Journal
Information Sciences: an International Journal
Modeling of plasma process data using a multi-parameterized generalized regression neural network
Microelectronic Engineering
Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process
Information Sciences: an International Journal
A revisited approach to linear fuzzy regression using trapezoidal fuzzy intervals
Information Sciences: an International Journal
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
Learning approaches for developing successful seller strategies in dynamic supply chain management
Information Sciences: an International Journal
Computers and Operations Research
Robust fuzzy regression analysis
Information Sciences: an International Journal
Information Sciences: an International Journal
A Midpoint--Radius approach to regression with interval data
International Journal of Approximate Reasoning
SMART: Stream Monitoring enterprise Activities by RFID Tags
Information Sciences: an International Journal
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
Fuzzy least-absolutes regression using shape preserving operations
Information Sciences: an International Journal
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Kernel based nonlinear fuzzy regression model
Engineering Applications of Artificial Intelligence
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Fuzzy 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.