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Since the fuzzy cerebellar model articulation controller (FCMAC) uses linguistic variables, it is highly intuitive and easily comprehended. Despite the FCMAC's good local generalization capability for approximating nonlinear functions and fast learning, a normal FCMAC requires huge memory, and its dimension increases exponentially with the number of inputs. In order to overcome the memory explosion...
This paper proposes a novel anti-swing control strategy for an overhead crane. The controller includes both position regulation and anti-swing control. Since the crane model is not exactly known, fuzzy CMAC are used to compensate friction, gravity as well as the coupling between position and anti-swing control. Real-time experiments are presented comparing this new stable anti-swing PD control strategy...
Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs, and it is difficult for its static structure to model a dynamic system. In this paper, we use two types...
The conventional fuzzy CMAC neural networks perform well in terms of their fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires an enormous memory and the dimension increase exponentially with the input number. In this paper, we use two techniques to overcome these problems: recurrent and hierarchical structures and propose a new CMAC,...
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