Feasibility of Training and Investigation into Training of Sigmoidal FFANN with Gaussian Learning Rate and Zero Weight Initializations
نویسنده
چکیده
Artificial Neural Networks(ANN) has applications in the various fields. Non linear transformation problems can be solved using ANN. Generally the parameter weight is initialized to some random value. In this paper we have used Sigmoidal FFANN. The training is done using Gaussian learning rate and weights are initialized to zero. It is found that the training of Sigmoidal FFANN can be done even if the weights are initialized to zero.
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