@inproceedings{oai:kanazawa-u.repo.nii.ac.jp:00007559,
 author = {Hara, Kazuyuki and Nakayama, Kenji},
 book = {IEEE&INNS Proc. IJCNN'93, Nagoya},
 month = {Oct},
 note = {Frequency analysis capability of multilayer neural networks, trained by back-propagation (BP) algorithm is investigated. Multi-frequency signal classification is taken into account for this purpose. The number of frequency sets, that is signal groups, is 2approx.5, and the number of frequencies included in a signal group is 3approx.5. The frequencies are alternately located among the signal groups. Through computer simulation, it has been confirmed that the neural network has very high resolution. Classification rates are about 99.5% for training signals, and 99.0% for untraining signals. The results are compared with conventional methods, including Euclidean distance with accuracy of about 65%, Fourier transform with accuracy of about 10approx.30%, and using very high-Q filters with a huge number of computations. The neural network requires only the same number of inner products as the hidden units. Frequency sensitivity and robustness for the random noise are studied. The networks show high frequency sensitivity, namely, the networks have high frequency resolution. Random noise are added to the multi-frequency signals to investigate how does the network cancel uncorrelated noise among the signals. By increasing the number of samples, or training signals, effects of random noise can be cancelled.},
 pages = {601--604},
 publisher = {IEEE(Institute of Electrical and Electronics Engineers)},
 title = {Classification of multi-frequency signals with random noise using multilayer neural networks},
 volume = {1},
 year = {1993}
}