Fuzzy linear regression model with crisp coefficients: A dynamic optimization scheme

The fuzzy linear regression model with fuzzy input-output data and crisp coefficients is studied in this paper. a hybrid scheme based on recurrent neural networks is proposed to calculate the regression coefficients. Here a neural network is first constructed based on some concepts of convex optimization and stability theory. The presented neural network framework guarantees to obtain to find the approximate parameters of the fuzzy regression problem. The existence and convergence of the trajectories of the neural network are studied. The Lyapunov stability for the neural network is also shown. Some illustrative examples provide a further demonstration of the effectiveness of the method.