Research Article

Bio-Inspired Microsystem for Robust Genetic Assay Recognition

Define input vector to the first layer that contains input
patterns with −1 bias
For to iter
Calculate hidden neuron output using
sigmoid function
Define output vector from the hidden layer
that contains hidden neuron output and −1 bias
Calculate the final output using sigmoid 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
Compute the first back-propagation error set
Compute the second back-propagation error set
Update the second weight matrix
Update the first weight matrix
End
Algorithm 1