Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The application of connectionist architectures for fast and robust online learning of dynamic relations used in robot control at the executive hierarchical level is discussed. The proposed connectionist robot controllers use decomposition of robot dynamics. This method enables the training of neural networks on the simpler input/output relations with sigfnificant reduction of learning time. The other important features of these algorithms are fast and robust convergence properties because the problem of adjusting the ights of internal hidden units is considered as a problem of estimating parameters by the recursive least squares method and the extended Kalman filter approach. From simulation examples of robot trajectory tracking it is shows that when a sufficiently trained network is desired, the learning speed of the proposed algorithm is faster than that of the traditional backpropagation algorithms. >
['Dusko Katic', 'Miomir Vukobratovic']
I am writing a research paper on a idea Decomposed connectionist architecture for fast and robust learning of robot dynamics
[{"from": "human", "value": "I am writing a research paper on a idea Decomposed connectionist architecture for fast and robust learning of robot dynamics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The application of connectionist architectures for fast and robust online learning of dynamic relations used in robot control at the executive hierarchical level is discussed. The proposed connectionist robot controllers use decomposition of robot dynamics. This method enables the training of neural networks on the simpler input/output relations with sigfnificant reduction of learning time. The other important features of these algorithms are fast and robust convergence properties because the problem of adjusting the ights of internal hidden units is considered as a problem of estimating parameters by the recursive least squares method and the extended Kalman filter approach. From simulation examples of robot trajectory tracking it is shows that when a sufficiently trained network is desired, the learning speed of the proposed algorithm is faster than that of the traditional backpropagation algorithms. >"}]