TJHSST Computer Systems Lab Senior
Research Project: Excursions into Neural
Networks 2006-2007
Tianuhi Cai
October 1, 2007
Abstract
Artificial neural networks model the circuitry of biological neu-
rons. Inspired by the brain, they may share common properties with
their biological counterparts, although they may be distinctly differ-
ent. For example, the topology of the connections of neurons in an
Adaline network (single-layered backpropagation network) will not be
the same as those in a multi-layer backpropagation network. The size
and number of layers in a neural network also influence its accuracy
in its applications. In this project, different variations on neural net-
works will be tested and evaluated on their performance in recognizing
handwritten characters. Genetic algorithms and fuzzy logic may be
used for various applications.
Keywords: neural networks, computer vision, genetic algorithms
1 Introduction - Elaboration on the problem
statement, purpose, and project scope
1.1 Scope of Study
The work will consist of a comparison of multiple types of neural networks on
their performance in the recognition of handwritten characters. Handwriting
samples in the form of images will be needed to train and test the neural
1
networks. Training neural networks to recognize handwritten strokes in a
video format, rather than handwritten characters in an image format, may
be done if time and resources are sufficient. Various neuron classes and
neural network classes will be written in JAVA, which is suitable because of
its object-oriented capabilities.
The project will start out with a simple neuron and a one-layer neural
network class; as development progresses, other neuron and neural network
classes will be created, extending the original neuron and neural network
classes. In the beginning, the neural network will be simple and its perfor-
mance will be low. As variations are introduced, such as genetic algorithms,
the performance may improve.
1.2 Expected results
During the course of the research project, variat