ESNs)和TS模型提出一种新的模糊模型结构——模糊回声状态网络(Fuzzy Echo State Networks
FESNs).FESNs由多条TS类型的模糊规则组成
规则后件采用ESNs网络.研究表明
TS模型和ESN都可以看做是FESN模型的某种特例
而且FESNs具有较强的非线性映射能力、局部反馈以及学习算法稳定等特点.同时
其模型参数确定方法与经典TS模型以及ESN一样可以归结为一个线性回归问题
大大减少了网络训练的计算量.仿真实验表明
与经典TS模型相比
FESNs在不显著增加建模时间情况下可有效提高建模精度.
Abstract
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.