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Nm,n shows Neuron where, m = Layer Number & n = Neuron Number Wi,j,k represents Weight where, i=Layer No.., j=Neuron No., k=Output No.of the particular Neuron A Directional Feature with Energy based Offline Signature **Verification Network**
The proposed ANN scheme uses a multi layer feed forward network employing a back propagation learning algorithm with 16 Neurons in input layer and 1 Neuron in output layer. One hidden layer is present with 16 Neurons. The transfer function used for all the three layers are Hyperbolic Tangent Sigmoid (tansig). The proposed architecture of Neural Network is shown in Fig. 4. Here, default value of bias is chosen. Total 501 inputs i.e. 100 for energy of each segment, 400 as direction feature and 1 shows aspect ratio is given to this neural network. 