A NN Image Classification Method Driven by the Mixed Fitness Function

Shan Gai, Peng Liu, Jiafeng Liu, Xianglong Tang

Abstract


The mixed fitness function of the error sum squares linear transformation is proposed in the article, and this function can improve the evaluation method of the individual fitness, and combining with NN, this method can be used in the high-speed paper money image analysis system. Aiming at many characters such as the high comparability of paper money images of different denominations, small class distance and large in-class discreteness induced by the using abrasion, this method first codes the weight values and threshold values of NN with real values, and transforms the problem from the representation type to the genotype, and performs many genetic operations such as selecting, crossing and variation, and takes the weight value and threshold value trained by the genetic algorithm according to the individual fitness value of the mixed fitness function as the initial weight value and initial threshold value of NN in the next stage, and trains these values by NN to establish the sorter. This method was tested in the embedded system with resource restriction (TI TMS320C6713 DSP), and 20000 RMB images were acquired as the samples, and 12000 images of them were tested, and the test result indicated that the method combining improved genetic algorithm with NN obviously enhanced the recognition rate.


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Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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