tested. Meanwhile, the conventional ANN of Structure A was also tested for comparison.

[c ![width 1.5pt]{} c c|c c]{} & &\
& *Train* & *Test* & *Train* & *Test*\
Struct. A & 20.73 & 16.74 & 1.07 & 1.64\
Struct. B & 16.89 & 14.50 & &\

The experimental results are shown in Table \[3layerresult\]. On one hand, by using the proposed training method, finally, the BDNN converged on the training dataset and on the test dataset, the classification rate was around $15\%$. By this result, the feasibility of BDNN is proved. On the other hand, it can be seen that the conventional ANN of Structure A has much lower error rate than the BDNN of same structure. This is may be because the BDNN was not fully trained by the proposed method (borrowed from the conventional network training). If we can design specific training method for BDNN, the performance of which may be improved a lot. Another possible reason may be the complexity of the network. Since the binary neurons are used in BDNN, the complexity of which is lower than the conventional ANN, though they have the same structure. The result of BDNN of Structure B indicates that, 