1. Implementing a reliable neuro-classifier
Implementing a reliable neuro-classifier for paper currency using pca algorithm
老師 : 楊士萱 博士
學生 : 陳柏源 碩二

2. Outline
Introduction
Method
Conclusion
Reference

3. Introduction(1/1)
Using Principal Components Analysis (PCA) method in order to increase the reliability of paper currency recognition machines，which use neural network classifier.
A Learning Vector Quantization (LVQ) neural network model is used as the main classifier and a total number of 10 US bill types including 1、2、5、10、20、50 and 100 dollar (new and old model) are considered as classification categories
The experimental results show a 30% growth in reliability after using extracted features

4. Method(1/6) Preprocessing Feature extraction
Classification and reliability evaluating
Experimental results

5. Method(2/6) preprocessing data
Original image 10x170 array  6x30 array
5 sensor are used，each of them uses two different waves lengths for generating two channels of data
By using a linear function to generate a new channel of data base on two channel of each sensor.
Totally 15 channel are obtained among them we select 6 main channel.
A simple algorithm is used to reduce the size of data from 170 pixels in each channel to 30.
Normalization :

6. Method(3/6) feature extraction
Using PCA to extract the features of training data
Covariance matrix -> M eigenvector corresponding to the M largest eigenvalues
Select 30 main features

7. Method(4/6) classification
Kohonen’s LVQ is a supervised learning algorithm with the competitive network.
The network has a number of 30 neurons in the input layer and 400 neurons in the output layer.

8. Method(5/6) reliability evaluating
Using specific algorithm to evaluate the reliability of classification
All codebooks are drawn supposing a Gaussian distribution

9. Method(6/6) Experimental results
Training data : 3,570 sample data from 40 different class
Testing data : 1,200 sample (30 samples per class)

10. Conclusion(1/1)
Incrementing the number of codebooks will make the variance of data within each class and consequently the overlap zone between classes, to be decreased.
PCA increase the variance within the new components space, but as the distance between codebooks are increased, it makes the overlap between pro. densities to be significantly decreased and consequently the reliability of the system is improved.

11. Reference(1/1)
Ali Ahmadi,Sigeru Omatu,Michifumi Yoshioka,”Implementing a Reliable Neuro-Classifier for Paper Currency Using PCA Algorithm,”SICE 2002 Aug.5-7,2002,Osaka.