We investigate the ability of feedback-error-learning (FEL) algorithm through applying it to training a pan-tilt camera head for pursuing a moving target. Although the task of pursuit eye movement is re BLOCKINective and the control has a relatively large delay, our experiments show that the camera head can successfully acquire the skill of pursuit eye movement. Our experimental results also sh...

Feedback-error learning (FEL) neural network was developed for control of a powered trans-femoral prosthesis. Nonlinearities and time-variations of the dynamics of the plant, in addition to redundancy and dynamic uncertainty during the double support phase of walking, makes conventional control methods very difficult to use. Rule-based control, which uses a knowledge base determined by machine ...

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstr...

This study examined motivational effects of feedback on motor learning. Specifically, we investigated the influence of social-comparative feedback on the learning of a balance task (stabilometer). In addition to veridical feedback (error scores reflecting deviation from the target horizontal platform position) about their own performance after each trial, two groups received false normative inf...

Cognitive Neuroscience studies have identified an early warning system in the human brain that can avoid to make past mistakes again. They have shown how the brain remembers details about past dangers [1]. An activity was found in the Anterior Cingulate Cortex (ACC) after making mistakes [2]. This cortex area works as an early warning system that adjusts its behaviour to avoid dangerous situati...

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inp...