2000 Fiscal Year Final Research Report Summary
Classification of New and Used Bills by Acoustic Data Using Neural Networks
| Project/Area Number |
11450155
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B).
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
System engineering
|
|---|
| Research Institution | Osaka Prefecture University |
|---|
Principal Investigator |
OMATU Sigeru Osaka Prefecture University, Graduate School of Engineering Professor, 工学研究科, 教授 (30035662)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
FUJINAKA Toru Osaka Prefecture University, Graduate School of Engineering, Lecturer, 大学院・工学研究科, 講師 (90190058)
YOSHIOKA Michifumi Osaka Prefecture University, Graduate School of Engineering, Lecturer, 大学院・工学研究科, 講師 (70285302)
|
|---|
| Project Period (FY) |
1999 – 2000
|
| Keywords | competitive neural network / learning control / acoustic pattern classification / learning vector quantization / generalization / new and used bills classification |
|---|
| Research Abstract |
In this project, we have proposed an approach to realize an intelligent classification of new and used bill money from acoustic data via banking machines when bills are passed in those machines by using neural networks. The present project is to synthesize an intelligent classifier system based on various types of neural networks. Especially, we have adopted here three kinds of neural networks which are a layered network by the error back-propagation algorithm, a self-organizing map network, and learning vector quantization networks. To complete the project study, we have adopted the following approach to synthesize those intelligent classification systems :
(1) Feature Extraction of Acoustic Data from New and Used Bill
Using spectrum and cepstrum data, we have extracted the specific features of acoustic data obtained from the new and used bill money. Then using the self-organizing map neural network, we have classified those data into two classes which mean new bill category and used bill category.
(2) Optimization of Network Size by Genetic Algorithms
To enhance the generalization of the networks, we have applied the genetic algorithms to the layered neural networks and competitive learning networks such that the minimum cost could be obtained. Then we could reduce the network size as small as possible under some constraints.
(3) Classification by Learning Vector Quantization
To classify the acoustic data from new and used bills from the spectrum and cepstrum, we have trained the neural networks based on the learning vector quantization. Then we could obtain more than 90% classification results for test data set from real bills.
(3) Hardware Implementation of the Proposed System
To speed up the computation of the proposed algorithm and reduce the cost, we have developed the hardware of the proposed system. Then we could realized cheep and high speed hardware system to achieve the specification of the bills classification.
|
