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 |
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Algorithm 1 |