Learning Finite State Machines "With
نویسنده
چکیده
learn stable states by introducing discretization into the network and using a pseudogradient learning rule to perform training. The essense of the learning rule is that in doing gradient decent, it makes use of the gradient of a sigmoid function as a heuristic hint in place of that of the hard-limiting function, while still using the discretized value in the feedback update path. The new structure uses isolated points in activation space instead of vague clusters as its internal representation of states. It is shown
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