Classification of New and Used Bills by Acoustic Data Using Neural Networks

在这个项目中，我们提出了一种方法来实现一个智能分类的新的和使用的钞票通过银行机的声音数据时，通过这些机器通过使用神经网络的法案。本项目是综合一个基于各种类型神经网络的智能分类器系统。特别是，我们在这里采用了三种神经网络，这是一个分层网络的误差反向传播算法，自组织映射网络，学习矢量量化网络。为了完成本课题的研究，我们采用了以下方法来综合这些智能分类系统：（1）新旧票据声学数据的特征提取利用频谱和倒频谱数据，提取出新旧票据声学数据的特定特征。然后利用自组织映射神经网络将这些数据分为两类，即新票据类和旧票据类。(2)用遗传算法优化网络规模为了提高网络的泛化能力，我们将遗传算法应用于分层神经网络和竞争学习网络，以获得最小代价。然后我们可以在一定的约束条件下尽可能地减小网络的规模。(3)为了从频谱和倒频谱中对新旧钞票的声学数据进行分类，我们基于学习矢量量化训练了神经网络。对真实的票据的测试数据集，我们可以得到90%以上的分类结果。(3)系统的硬件实现为了加快算法的运算速度和降低成本，我们开发了系统的硬件。实现了低成本、高速度的硬件系统，达到了票据分类的要求。

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 BillUsing 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 AlgorithmsTo 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 QuantizationTo 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 SystemTo 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.