A Novel Approach to Self-Adaptation of Neuro-fuzzy Controllers in Real Time

Authors:
Héctor Pomares;Ignacio Rojas;Jesús González;Miguel Damas
Affiliations:
-;-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Year:
2001

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Abstract

In this paper, we present a novel approach to achieve global adaptation in neuro-fuzzy controllers. The adaptation process is achieved by means of two auxiliary systems: the first one is responsible for adapting the consequents of the main controller's rules with the target of minimizing the error arising at the plant output. The second auxiliary system compiles real input/output data obtained from the plant, which are then used in real time taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy avoiding overfitting.