As everyone knows, multiple regression analysis is an important approach to prediction studies. However, regression model has some limitations and constraints in the real world practices. This study applied fuzzy regression using neural network (FRNN) to predict organizational performance, and the findings indicate that the accuracy rate analysis supported FRNN to be a better method to predict ...

In order to solve switching regression problems, many approaches have been investigated. In this paper, an integrated fuzzy clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering is presented. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton’s Gravity Law. The theoretic analysis shows that GFC can converge to a ...

this study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. a least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. the proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by...

The aim of this paper is to propose new mining techniques by which we can study the impact of different risk management techniques and different software risk factors on software analysis development projects. The new mining technique uses the fuzzy multiple regression analysis techniques with fuzzy concepts to manage the software risks in a software project and mitigating risk with software pr...

We explore attribute dependencies in the datasets by using direct and inverse fuzzy transforms. Our algorithm optimizes the fuzzy partitions of the universe of the attributes and moreover establishes if the set of the data points is sufficiently dense with respect to the chosen partitions: two specific regression indexes measure the reliability of our model. The known “El Nino” dataset is the b...

In this paper, we deal with the problem of computing the maintenance costs of electrical medium line in spanish towns. To do so, we present two Data Analysis tools taking as a base Evolutionary Algorithms, the Interval Genetic Algorithm-Programming method to perform symbolic regression and Genetic Fuzzy Rule-Based Systems to design fuzzy models, and use them to solve the said problem. Results o...

The fuzzy regression has been found effective in modeling the relationship between the dependent variable and independent variables when a high degree of fuzziness is involved and only a few data sets are available for model building. This research, therefore, proposes an approach for optimizing multiple responses in the Taguchi method using fuzzy regression and desirability function. The stati...

This paper introduces a new fuzzy clustering method with time-domain-constraints which is used to signal analysis. Proposed method makes it possible to include natural constraints for signal analysis using fuzzy clustering, that is, the neighboring samples of signal belong to the same cluster. This method can be called time-domainconstrained fuzzy clustering. This paper introduces two approache...