Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Towards Semi-supervised Manifold Learning: UKR with Structural Hints
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Visual servoing of redundant manipulator with Jacobian matrix estimation using self-organizing map
Robotics and Autonomous Systems
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
Journal of Intelligent and Robotic Systems
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This paper presents a review of self-organizing feature maps (SOFMs), in particular, those based on the Kohonen algorithm, applied to adaptive modeling and control of robotic manipulators. Through a number of references we show how SOFMs can learn nonlinear input–output mappings needed to control robotic manipulators, thereby coping with important robotic issues such as the excess degrees of freedom, computation of inverse kinematics and dynamics, hand–eye coordination, path-planning, obstacle avoidance, and compliant motion. We conclude the paper arguing that SOFMs can be a much simpler, feasible alternative to MLP and RBF networks for function approximation and for the design of neurocontrollers. Comparison with other supervised/unsupervised approaches and directions for further work on the field are also provided.