Bio-Inspired Microsystem for Robust Genetic Assay Recognition

Define input vector that contains input patterns 𝑋

with −1 bias

For 𝑖=1 to iter

 Calculate the hidden neuron output according to where

the net weighted input is falling in the range of the

piecewise sigmoid-logarithmic function

 Define output vector from the hidden layer that

contains hidden neuron output and −1 bias

 Calculate the final neuron output, the first back-

propagation error set, and the second back-propagation

error set according to where the net weighted input is

falling in the range of the piecewise sigmoid-logarithmic

function

 Check the criterion of percentage of the input data

       that has an error less than 20%

 If all input data have errors less than 20%,

 stop the training

 Update the second weight matrix

 Update the first weight matrix

End

Algorithm 2