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Analog Stable Simulation of Discrete Neural Networks
, 1997
"... The finite discretetime recurrent neural networks are also exploited for potentially infinite computations (e.g. finite automata) where the input is being gradually presented from an external environment via input neurons. Because of gradient learning heuristics or analog hardware implementation re ..."
Abstract

Cited by 3 (2 self)
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The finite discretetime recurrent neural networks are also exploited for potentially infinite computations (e.g. finite automata) where the input is being gradually presented from an external environment via input neurons. Because of gradient learning heuristics or analog hardware implementation reasons the usage of some continuous activation function is sometimes preferred rather than the discrete hard limiter (threshold function). However, in such cases the approximate representation of finite automaton states by analog network states can lead to an unstable behavior for long input sequences and consequently, to an incorrect resulting computation. Therefore, a stable simulation of any discrete neural network by an analog network of the same size is proposed. The simulation works in real time (`step per step') for any real activation function with different finite limits in improper points. In fact, only the weight parameters of the analog neural network are adjusted to achieve suffi...