Abstract
A two-jointed arm is used to discuss the conditions under which a neural controller can acquire a precise internal model of a plant to be controlled without the help of an external superviser. The problem can be solved by a ‘modified Hebbian rule’ ensuring convergence of the synaptic strengths, a feedforward network called ‘power network’, and a learning algorithm called ‘auto-imitation’. The modified Hebbian rule describes a neuron, that — in addition to a number of inputs with plastic weights — has also a teaching input established with a fixed synaptic weight. The power network can adopt accurate models even of non-linear plants like the two-jointed arm when established with modified Hebbian synapses. The auto-imitation algorithm provides the power network with the values to be achieved by the network after learning. The training must be able to generate arbitrary movements, first of low velocity, then of higher velocity.
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Kalveram, K.T. (2000). Sensorimotor Sequential Learning by a Neural Network Based on Redefined Hebbian Learning. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_41
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_41
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