The parameter h in a fuzzy linear regression model is vital since it influences the degree of the fitting of the estimated fuzzy linear relationship to the given data directly. However, it is usually subjectively preselected by a decision-maker as an input to the model in practice. In Liu and Chen (2013), a new concept of system credibility was introduced by combining the system fuzziness with ...

Neural-fuzzy systems have been proved to be very useful and have been applied to modeling many humanistic problems. But these systems also have problems such as those of generalization, dimensionality, and convergence. Support vector machines, which are based on statistical learning theory and kernel transformation, are powerful modeling tools. However, they do not have the ability to represent...

An algorithm for real-time prediction of river stage dynamics using a Takagi-Sugeno fuzzy system is presented in this paper. The system is trained incrementally each time step and is used to predict onestep and multi-step ahead of river stages. The number of input variables that were considered in the analysis was determined using two statistical methods, i.e. autocorrelation and partial autoco...

The software tools are described that are designed for multivariate linear regression analysis of fuzzy data. The underlying method of f-regression is thoroughly described in [4]. The tools can be used for modeling both linear relationships and relationships that are nonlinear by nature, but linear in the parameters. Source data can be both crisp and fuzzy. The solution of a technical problem i...

This paper presents the comparative study for fuzzy regression model using linear programming, fuzzy regression model using genetic algorithms and standard regression model. The fuzzy and standard models were developed for estimation of electric power losses in electrical networks. Simulation was carried out with a tool developed in MATLAB.

This paper presents a novel method for estimating “total” predictive uncertainty using machine learning techniques. By the term “total” we mean that all sources of uncertainty are taken into account, including that of the input and observed data, model parameters and structure, without attempting to separate the contribution given by these different sources. We assume that the model error, whic...