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Contents 1 Artifical Neural Network Modeling of the Molecular Biosensor 1.1 Training using back propagation 1.2 General error function 1.3 Sigmoid function

Artifical Neural Network Modeling of the Molecular Biosensor

Training using back propagation

:

Select function in matlab library to fit data

Find error, and compare to target error

General error function

Finally select function that gives least error

Sigmoid function

An enose is an analytic device originally used for detecting chemicals and their concentrations in vapors

Using E-Nose allow us to apply it to a metal sensor by finding the functional relationship, which is the response to the concentration and type of metal

Now that we have built our Molecular Sensor , with E-Nose we can:
Use experimental data for individual metal ion protein sequence, and ligations

Look at a wider range of variation in the concentrations

Collect data from rejected samples to determine the reliability of network

And use the completion of the final network to identify the ion as well as it’s corresponding concentration

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