Recurrent fuzzy neural networks

which is usually trained by gradient descent

have some inherent shortcomings such as inefficiency of training process

local minimum.In this paper

we proposed a novel fuzzy neural network based on Echo state network and TS fuzzy model

called fuzzy echo state network (FESN)

which is a generality of both TS modal and ESN.FESNs consist of several fuzzy IF-THEN rules

each of which has a ESN as consequent part.We illustrate that FESNs have some interesting characteristics

such as better nonlinear mapping capacity

local feedback and stable learning

which results in that FESNs can deal with dynamics of nonlinear system.Furthermore

similar to the TS model and ESN

parameters of the FESNs can be determined by solving a linear regression problem

which dramatically reduce the computing burden of the training process.Experiments shows that FESN can effectively enhance the accuracy of modeling dynamical system at the expense of not using excessive additional time compared with TS model and ESN.