E-Handrawn Calculator
This draft for final report is about my research progress in developing e-Hand-Drawn Calculator as final year project. The purpose of this project is to demonstrate an application of back-propagation network (comparison of training their algorithms and transfer function) in order to developing e-...
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| Main Author: | |
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Universiti Teknologi Petronas
2008
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| Online Access: | http://utpedia.utp.edu.my/7161/1/2008%20-%20E%20-handrawn%20calculator.pdf http://utpedia.utp.edu.my/7161/ |
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| Summary: | This draft for final report is about my research progress in developing e-Hand-Drawn
Calculator as final year project. The purpose of this project is to demonstrate an application
of back-propagation network (comparison of training their algorithms and transfer function)
in order to developing e-Hand-Drawn Calculator. Back-propagation network is a supervised
learning method, and is an implementation of the Delta rule. It requires a teacher that knows,
or can calculate, the desired output for any given input. It is most useful for feed-forward
networks (networks that have no feedback, or simply, that have no connections that loop).
The term is an abbreviation for "backwards propagation of errors". Backpropagation requires
that the activation function used by the artificial neurons (or "nodes") is differentiable. The
main activities in this project are Assemble the training data, Create the network object, Train
the network and Simulate the network response to new inputs. The training data consist of
tern sample of each number zeros until number nine and symbol plus, minus, division and
multiplication. These all data will be train, testing and validation before enter to next stage
which is creating network object. This section presents the architecture of the network that is
most commonly used with the backpropagation .algorithm; the multilayer feedforward
network. The investigation for combination of Neuron Model (tansig, logsig, purelin) and
training algorithms (traingd, traingdm, traingda, traingdx, trainrp, traincgp, traincgb, trainscg,
trainbfg, trainoss, trainlm, trainbr); tend to know which combination will give the greatest
result and smallest error. |
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