Sentence examples for variance for fuzzy from inspiring English sources

In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network.

Section 3 presents the calculation process of the accurate variance for fuzzy variables.

Apart from the concept and calculation of the expected value, Carlsson and Fullér [8] brought up the notions of crisp possibilistic expected value and crisp possibilistic variance for fuzzy variables with continuous possibility distributions.

According to the definition of the variance for fuzzy variables, in this section, we present a calculation of the variance for three kinds of commonly used fuzzy variables, that is, the symmetric triangular fuzzy number, the asymmetric triangular fuzzy number, and the Gaussian fuzzy number.

Now let us show a calculation process of the variance for some fuzzy variables via Eq. (18) and also the corresponding credibility distributions.

Following this idea, the calculation of the accurate value of the variance for some special fuzzy variables, like the symmetric and asymmetric triangular fuzzy numbers and the Gaussian fuzzy numbers, is presented in this paper, which turns out to be far more complicated.

Thus, in this paper, we studied the variance of fuzzy variables based upon the credibility measure for better applications.

We can see from Section 3 that the whole calculation process of the variance for a single fuzzy variable is not easy even for the simplest symmetric triangular fuzzy number.

This paper focuses on the variance of fuzzy variables with regular credibility distributions.

In "Analysis of variance based on fuzzy observations", a new method for ANOVA test for fuzzy observations is provided and discussed.

To construct this gene set, genes with FPKM > 5 in any section or mean FPKM > 3 across all sections (6315 genes) were ranked by variance across all sections, and the top 20% were retained for fuzzy C-means analysis (1263 genes).