Classification of Battlefield Ground Vehicles Based on the Acoustic Emissions

Genetic Optimization of Neural Networks for Person Recognition based on the Iris

“…chosen to optimize this integration system was a algorithm a better recognition rate was achieved integration system were obtained in the modular neural network. As comparison of results, we can mention that in [47] a 93.33% recognition rate was while in this work we were able to obtain recognition rates of 99.76%. This fact shows that the proposed approach can outperform similar neural approaches in the literature for iris for person recognition using the iris biometric measurement modular neural network architectures with 3 layers in each neurons in the first hidden layer, 116 and 113 in the 2nd the third hidden layer.…”

“…After applying the recognition rate was achieved, because better results with were obtained in the modular neural network. As comparison of results, we can mention that in [47] a 93.33% recognition rate was while in this work we were able to obtain recognition rates of 99.76%. This fact shows that the proposed approach can outperform similar neural approaches in the literature for iris biometric measurement was layers in each module, hidden layer, and each generation of the a recognition rate of 98.48%, with an average error of 0.0202, and for or validation of the gating network with Triangular type 99.37%, and for the this integrator was on for the validation with triangular type Gaussian type MFs the integration were not optimize the membership The evolutionary method After applying the genetic results with the optimized ISSN: 1693-6930…”

“…As comparison of results, we can mention that in [47] a 93.33% recognition rate was achieved, while in this work we were able to obtain recognition rates of 99.76%. This fact shows that the proposed approach can outperform similar neural approaches in the literature for iris recognition.…”