Analysis of the DNN-kWTA Network Model with Drifts in the Offset Voltages of Threshold Logic Units
The structure of the dual neural network-based (DNN) k-winner-take-all (kWTA) model is much simpler than that of other kWTA models. Its convergence time and capability under the perfect condition were reported. However, in the circuit implementation, the threshold levels of the threshold logic units (TLUs) in the DNN-kWTA model may have some drifts. This paper analyzes the DNN-kWTA model under the imperfect condition, where there are some drifts in the threshold level. We show that given that the inputs are uniformly distributed in the range of [0, 1], the probability that the DNN-kWTA model gives the correct output is greater than or equal to \((1-2\varDelta )^n\), where \(\varDelta \) is the maximum drift level. Besides, we derive the formulas for the average convergent time and the variance of the convergent time under the drift situation.
KeywordsWinner-take-all Dual neural network Threshold logic unit Convergence
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