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The cerebellar model articulation controller (CMAC) neural network is capable of learning nonlinear functions extremely quickly due to the local nature of its weight updating. The rectangular shape of CMAC receptive field functions, however, produces discontinuous (staircase) function approximations without inherent analytical derivatives. The ability to(More)
A methodology is presented for integrating artificial neural networks and knowledge-based systems for the purpose of robotic control. The integration is patterned after models of human motor skill acquisition. The initial control task chosen to demonstrate the integration technique involves teaching a two-link manipulator how to make a specific type of(More)
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