A Dual Neural Network Based On Confidence Intervals For Fuzzy Random Regression Problems

Uncertainty in dependent variables or independent variables is typically caused by randomness or fuzziness. But randomness and fuzziness are more and more often appearing simultaneously in independent variables or dependent variables, giving rise to the concept of a fuzzy random variable. Regression analysis is a statistical measure to model the relationship between a dependent variable and one or more independent variables. However, the standard regression algorithms cannot handle the fuzzy random variables, so we propose a dual neural network algorithm based on confidence intervals for fuzzy random regression problems in this paper. The algorithm relies on the expectations of, and variances in, fuzzy random variables to construct the confidence intervals for fuzzy random input-output data. A dual neural network then identifies the sides of the interval output data; one network identifies the upper side, another network identifies the lower side, while a dual v-support vector regression algorithm concurrently constructs the initial structure of the dual neural network. Lastly, a dynamic genetic backpropagation algorithm tunes the parameters of the dual neural network to improve performance. Experiment results demonstrate the validity and applicability of the proposed dual neural network algorithm based on confidence intervals.