Case study : an effect of noise in character recognition system using neural network
There has been resurgence of interest in artificial neural networks over the past few years, as a researchers from diverse backgrounds have produced a firms theoretical foundation and demonstrated numerous applications of this rich field of study. Neural networks are useful tools for solving many...
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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English English |
| 出版: |
2003
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| 主題: | |
| 在線閱讀: | http://psasir.upm.edu.my/id/eprint/8706/1/FSKTM_2003_8%20IR.pdf |
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| 總結: | There has been resurgence of interest in artificial neural networks over the past few years,
as a researchers from diverse backgrounds have produced a firms theoretical foundation
and demonstrated numerous applications of this rich field of study. Neural networks are
useful tools for solving many type of problems. These problems may be characterized as
mapping(including pattern association and pattern classification), clustering and
constrained optimization.
There has been great deal of work on enhancing neural network performance. Two
important parameter are convergence and generalization. Convergence is the amount of
time, measured in CPU operations or training epochs, required to find an acceptable solution for training. Generalization measures the ability to correctly classify new unseen
data.
This project studies the generalization ability of trained network to classify noisy data.
The aim of this project is to develop a network that is able to recognize various inputs
through a series of simulation using Neural Network simulator called MATLAB. The
effect of the created network with noise are seen. This projects uses the most popular
training method in character recognition problem, namely backpropagation algorithm.
The theoretical foundation of this algorithm will be studied and summarized. Simulation
experiment results on training and testing data will be recorded and discussed. |
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