Traditional approaches for modeling TSK fuzzy rules are trying to adjust the parameters in models, and not considering the training data distribution. Hence it will result in an improper clustering structure, especially, when outliers exist. In this paper, a clustering algorithm termed as Robust Proper Structure Fuzzy Regression Algorithm (RPSFR) is proposed to define fuzzy subspaces in a fuzzy...

The approach of continuous evaluation is an important methodology in educational learning process. However, only recently it was applied in training based on virtual reality. This paper presents a methodology of evaluation that uses a fuzzy continuous evaluation approach to provide a user profile from his several training. This information can be used to improve the user performance in the real...

Pedomodels have become a popular topic in soil science and environmentalresearch. They are predictive functions of certain soil properties based on other easily orcheaply measured properties. The common method for fitting pedomodels is to use classicalregression analysis, based on the assumptions of data crispness and deterministic relationsamong variables. In modeling natural systems such as s...

Fuzzy linear regression analysis with symmetric triangular fuzzy number coefficient has been introduced by Tanaka et al. In this work we propose to approximate the fuzzy nonlinear regression using Artificial Neural Networks. The working of the proposed method is illustrated by the case study with the data for temperature and evaporation for the IARI New Delhi division. MSC: 62J86

in this paper, a new approach of modeling for artificial neural networks (ann) models based on the concepts of ann and fuzzy regression is proposed. for this purpose, we reformulated ann model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ann models. hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility. in ...

The aim of this discussion is to introduce a new fuzzy regression model, based on the distance between the outputs of the model in terms of its measurements along with the optimal confidence level ‘h’ using the shape preserving operations. Simple fuzzy regression models with fuzzy inputfuzzy outputs are also considered in which the coefficients of the models are themselves triangular fuzzy numb...

In order to efficiently improve the prediction accuracy, two load forecasting model based on fuzzy theory are presented, which are fuzzy clustering model and improved fuzzy regression analysis model .The method of fuzzy clustering is used to divide the area by the similar feature of load increasing. The new division is promising to improve the result of evident degree of clustering index to pow...

This paper presents a method based on fuzzy regression to analyze fatigue crack growth data, where the variations of the parameters are not only due to measurement errors but also system errors. A membership function is used to describe the system errors. Crack growth data under constant and random amplitude stress are analyzed and the results are compared with conventional least-squares regres...