Brief paper: A universal iterative learning stabilizer for a class of MIMO systems
Automatica (Journal of IFAC)
Selecting the right MBA schools - An application of self-organizing map networks
Expert Systems with Applications: An International Journal
Robotics and Autonomous Systems
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
The framework of controlled active vision
Mathematical and Computer Modelling: An International Journal
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The implementation of two neural network algorithms for visuo-motor control of an industrial robot (Puma 562) is reported. The first algorithm uses a vector quantization technique, the `neural-gas' network, together with an error correction scheme based on a Widrow-Hoff-type learning rule. The second algorithm employs an extended self-organizing feature map algorithm. Based on visual information provided by two cameras, the robot learns to position its end effector without an external teacher. Within only 3000 training steps, the robot-camera system is capable of reducing the positioning error of the robot's end effector to approximately 0.1% of the linear dimension of the work space. By employing adaptive feedback the robot succeeds in compensating not only slow calibration drifts, but also sudden changes in its geometry. Hardware aspects of the robot-camera system are discussed