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A neural network was designed and coded to identify decimal digits using Visual Basic and Excel. It was hypothesized that the neural network would have a 75% accuracy in recognizing the digits.

In this project, a neural network was designed and coded to identify decimal digits using Visual Basic and Excel. It was trained with twenty examples of each digit, for a total number of 200 trials. After 30,000 iterations of the training data, the neural network was able to identify the training set of digits with a 90% accuracy. The results of the experiment data showed that the network could identify new patterns of digits with an 83% accuracy.

Title : Neurotic About Neurons

Objectives/Goals

A neural network was designed and coded to identify decimal digits using Visual
Basic and Excel. It was
hypothesized that the neural network would have a 75% accuracy in recognizing
the digits.

Methods/Materials

The neural network was composed of three layers: the input layer, the hidden
layer, and the output layer.
The neural network was trained with twenty examples of each digit, for a total
number of 200 trials. The
input pattern for each digit was inserted into the neural network in the form of
an eight by four array of
data, and the neural network generated an answer. The back propagation algorithm
was used to train the
neural network, iterating 30,000 times and achieving a 0.01 mean square error.

Results

The neural network was able to identify the training set of digits with a 90%
accuracy. The results of the
training data showed that the network learned properly and identified the digits
with a high accuracy.
Next, experimental data was created by people who had not seen the original
training data. After all the
experimental data was propagated through the neural network, the results showed
that it identified the
experimental data with an 83% accuracy. The experiment data results showed that
the network could
identify new patterns of digits that had not been propagated through the network
in the training data. The
network adapted to the new information and identified the digits with a high
accuracy. To achieve those
results, 30,000 iterations of the training data were executed in order to reduce
the mean square error as
much as possible so that the network could identify new patterns properly. The
learning rate was also
lowered so that the weights were adjusted by smaller increments. Although this
lengthened the
convergence time, it was necessary to prevent false minimum errors in the
network.

Conclusions/Discussion

The neural network was successfully created and identified the decimal digits
from the training and
experiment data. The hypothesis stated that if a neural network was trained to
identify decimal digits,
then it would be able to identify the digits with a 75% accuracy. The results
prove that the hypothesis is
valid because the neural network correctly identified the experiment data with
an 83% accuracy.

Summary Statement

A neural network was designed and coded to identify decimal digits using Visual
Basic and Excel.

Help Received

My father helped me to understand the back propagation math, and he bought me a
new computer to
finish the project. My mother and sister helped to create the experiment data.
Professor Michael
Crowley, from USC, gave me some suggestions for my project.

Neurotic About Neurons

Can neural networks identify decimal digits using visual basic and excel?

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Math is all about numbers, patterns, and solving problems. Computer science is about using math and technology to create computer programs and solve real-world problems. Together, they help us understand how things work, how to solve problems, and how to use technology to make our lives better!

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