Study of Currency Recognition Based on Negative Correlation and Neural Network Ensemble
Jianbiao He · Video Engineering · 2013
In order to improve the recognition rate of currency,a negative correlation learning algorithm is proposed to improve the generalization ability of the neural network ensemble.The notes picture under UV-light is used as experimental samples in this paper.Ensemble neural network based on negative correlation learning algorithm is used for the classifier design.6 kinds of denomination notes in different noise are selected under a total of 300 as the training sample.By using MATLAB to simulate the single neural network classifier and neural network ensemble classifier,the simulation of reliability and recognition rate are compared.The experiment result shows that,the neural network ensemble based on negative correlation learning is good for currency recognition classification,compared with the system using single neutral network and the integrated neural network of individual network of independent training,it has higher recognition rate of 4% in average.