System identification-A survey
Automatica (Journal of IFAC)
Training Product Unit Neural Networks with Genetic Algorithms
IEEE Expert: Intelligent Systems and Their Applications
Comparing a brain-inspired robot action selection mechanism with winner-takes-all
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Predicting the behaviour of G-RAM networks
Neural Networks
NEUROM: a ROM based RNS digital neuron
Neural Networks
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Learning convergence is demonstrated for networks of nodes which are defined by a population of values at the vertices of the n-dimensional hypercube. These are functionally equivalent to higher order nodes or sigma-pi units but have the potential to be implemented in readily available memory components. The cube based structure also offers insight into the process of generalization. Three algorithms are discussed: reward-penalty, back propagation, and a new one based on system identification in control theory. It is shown that reward-penalty style techniques emerge as a special case of system identification.