Radial basis function (RBF) network can be viewed
as a fuzzy rule base with specified membership functions and fuzzy inference operations. However, there
is a trade-off between the approximation performance of RBF network and the number of hidden
neurons. To tackle this problem, this paper proposes
a dynamic RBF (DRBF) network with a constructive
learning. This DRBF network not only can create the
new hidden neurons, but also can prune the insignificant hidden neurons. Then, an adaptive dynamic
RBF fuzzy neural control (ADRFNC) system, including a neural controller and a saturation compensator, is developed. The neural controller uses a
DRBF network to on-line mimic an ideal controller
and the saturation compensator is designed to dispel
the approximation error introduced by the neural
controller. Finally, the proposed ADRFNC system is
applied to a chaotic circuit and a DC motor control
system. Simulation and experimental results show the
proposed ADRFNC system can achieve a favorable
tracking performance when the controller’s parameters have been learned and the network structure has
been constructed by the proposed learning algorithm
關聯:
International Journal of Fuzzy Systems 13(3), p.175-184