A Fuzzy Elman Neural Network
Ling Li, Zhidong Deng, and Bo Zhang
The State Key Lab of Intelligent Technology and Systems
Dept. of Computer Science, Tsinghua University
Beijing 100084, China
Abstract -- A fuzzy Elman neural network (FENN) is proposed to identify and
simulate nonlinear dynamic systems. Each of all the fuzzy rules used in FENN has a
linear state-space equation as its consequence and the network, by use of firing strengths
of input variables, combines these Takagi-Sugeno type rules to represent the modeled
nonlinear system. The context nodes in FENN are used to perform temporal recurrence.
An online dynamic BP-like learning algorithm is derived. The pendulum system is
simulated as a testbed for illustrating the better learning and generalization capability of
the proposed FENN network, compared with the common Elman-type networks.
Keywords -- nonlinear dynamic system modeling, fuzzy neural networks, Elman
networks, BP-like learning algorithm.
Artificial neural networks (ANNs), including fuzzy neural networks (FNNs), are
essentially nonlinear. They have already been used to identify, simulate and control
nonlinear systems [1,2] and have been proved to be universal approximators [3,4,5]. As