Abstract
This paper describes a self-learning ULR fuzzy controller using temporal back propagation. The ULR fuzzy controller is a multi-layer feed-forward network in which each node performs an unidirectional linear response (ULR) function (node function) on incoming weighted signals. In order to achieve a desired input-output mapping, the weight parameters are updated with a temporal back propagation such that the state variables can follow a given desired trajectory as closely as possible. The temporal back propagation algorithm is used to train the ULR fuzzy controller to a variety of problems. We demonstrate the effectiveness of the self-learning ULR fuzzy controller by applying it to a benchmark problem in intelligent control-the inverted pendulum system. Experiments show a very good control performance and self-learning capability of the ULR fuzzy controllers.
