A “mutual update” training algorithm for fuzzy adaptive logic control/decision network (FALCON)

The conventional two-stage training algorithm of the fuzzy/neural architecture, called FALCON, may not provide accurate results for certain type of problems, due to the implicit assumption of independence that this training makes about parameters of the underlying fuzzy inference system. In this paper, a training scheme is proposed for this fuzzy/neural architecture, which is based on line search methods that have long been used in iterative optimization problems. This scheme involves synchronous update of the parameters of the architecture corresponding to input and output space partitions and rules defining the underlying mapping; the magnitude and direction of the update at each iteration is determined using the Armijo rule. In our motor fault detection study case, the mutual update algorithm arrived at the steady-state error of the conventional FALCON training algorithm is twice as fast and produced a lower steady-state error by an order of magnitude