Least squares model fitting to fuzzy vector data
Fuzzy Sets and Systems
Information Sciences: an International Journal
Fuzzy linear regression analysis for fuzzy input-output data
Information Sciences: an International Journal
A generalized fuzzy weighted least-squares regression
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Fuzzy set-theoretic methods in statistics
Fuzzy sets in decision analysis, operations research and statistics
Fuzzy sets in decision analysis, operations research and statistics
Evaluation of fuzzy linear regression models by comparing membership functions
Fuzzy Sets and Systems
A least-squares approach to fuzzy linear regression analysis
Computational Statistics & Data Analysis
Fuzzy regression methods—a comparative assessment
Fuzzy Sets and Systems
Fuzzy regression model with fuzzy input and output data for manpower forecasting
Fuzzy Sets and Systems
Hybrid fuzzy least-squares regression analysis and its relibabilty measures
Fuzzy Sets and Systems
Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data
Computational Statistics & Data Analysis
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Fuzzy linear regression analysis from the point of view risk
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Machine-learning paradigms for selecting ecologically significant input variables
Engineering Applications of Artificial Intelligence
Multiple regression with fuzzy data
Fuzzy Sets and Systems
Asymptotic properties of least squares estimation with fuzzy observations
Information Sciences: an International Journal
Regression with fuzzy random data
Computational Statistics & Data Analysis
Information Sciences: an International Journal
Insight of a fuzzy regression model
Fuzzy Sets and Systems
Information Sciences: an International Journal
A revisited approach to linear fuzzy regression using trapezoidal fuzzy intervals
Information Sciences: an International Journal
Robust fuzzy regression analysis
Information Sciences: an International Journal
Maximum likelihood estimation from fuzzy data using the EM algorithm
Fuzzy Sets and Systems
International Journal of Approximate Reasoning
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Recent years have seen a surge of interest in extending statistical regression to fuzzy data. Most of the recent fuzzy regression models have undesirable performance when functional relationships are nonlinear. In this study, we propose a novel version of fuzzy regression model, called kernel based nonlinear fuzzy regression model, which deals with crisp inputs and fuzzy output, by introducing the strategy of kernel into fuzzy regression. The kernel based nonlinear fuzzy regression model is identified using fuzzy Expectation Maximization (EM) algorithm based maximum likelihood estimation strategy. Some experiments are designed to show its performance. The experimental results suggest that the proposed model is capable of dealing with the nonlinearity and has high prediction accuracy. Finally, the proposed model is used to monitor unmeasured parameter level of coal powder filling in ball mill in power plant. Driven by running data and expertise, a strategy is first proposed to construct fuzzy outputs, reflecting the possible values taken by the unmeasured parameter. With the engineering application, we then demonstrate the powerful performance of our model.