IEEE Trans Neural Netw Learn Syst - Properties and Performance of Imperfect Dual Neural Network-Based k WTA Networks.

Tópicos

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Resumo

The dual neural network (DNN)-based k -winner-take-all ( k WTA) model is an effective approach for finding the k largest inputs from n inputs. Its major assumption is that the threshold logic units (TLUs) can be implemented in a perfect way. However, when differential bipolar pairs are used for implementing TLUs, the transfer function of TLUs is a logistic function. This brief studies the properties of the DNN- k WTA model under this imperfect situation. We prove that, given any initial state, the network settles down at the unique equilibrium point. Besides, the energy function of the model is revealed. Based on the energy function, we propose an efficient method to study the model performance when the inputs are with continuous distribution functions. Furthermore, for uniformly distributed inputs, we derive a formula to estimate the probability that the model produces the correct outputs. Finally, for the case that the minimum separation min of the inputs is given, we prove that if the gain of the activation function is greater than 1/4min max(ln2n, 2 ln1-/ ) , then the network can produce the correct outputs with winner outputs greater than 1- and loser outputs less than , where is the threshold less than 0.5.

Resumo Limpo

dual neural network dnnbase k winnertakeal k wta model effect approach find k largest input n input major assumpt threshold logic unit tlus can implement perfect way howev differenti bipolar pair use implement tlus transfer function tlus logist function brief studi properti dnn k wta model imperfect situat prove given initi state network settl uniqu equilibrium point besid energi function model reveal base energi function propos effici method studi model perform input continu distribut function furthermor uniform distribut input deriv formula estim probabl model produc correct output final case minimum separ min input given prove gain activ function greater min maxlnn ln network can produc correct output winner output greater loser output less threshold less

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