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Multiple self-organizing maps to facilitate the learning of visuo-motor correlations

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5 Author(s)
Buessler, J.L. ; TROP Res. group, Mulhouse Univ., France ; Kara, R. ; Wira, P. ; Kihl, H.
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This paper presents an application of bi-directional neural modularity: a chaining of several self-organizing maps (SOM) is used to represent the motor and sensorial position correlations of a robotic platform. Two active cameras follow the movements of a robot manipulator in 3-D space. The mapping of image positions and camera orientations into arm angular joint positions can be learned by a neural network. However, decomposing the problem and using several neural networks turns out to be a better way. In our approach, the neural modules do not need to be adapted independently. Based on the principle of bi-directionality, the modular architecture can be adapted globally, using the sensor-motor data directly

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Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on  (Volume:3 )

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