Fuzzy regression analysis by neural networks with non-symmetric fuzzy number weights

In this paper, we first explain the fuzzy regression methods based on fuzzy linear models with symmetric triangular fuzzy number coefficients, and point out some drawbacks in such fuzzy regression methods. Next, we extend the fuzzy linear models to the case of non-symmetric fuzzy number coefficients. We illustrate that several drawbacks can be remedied by this extension. We then propose three methods of fuzzy nonlinear regression analysis using fuzzified neural networks with non-symmetric fuzzy number weights. One of the proposed nonlinear fuzzy regression methods is applied to the determination of type 2 membership functions