title: Additive and multiplicative noise reduction by back propagation neural network.
abstract: A novel filter is proposed by applying back propagation neural network (BPNN) ensemble where the noisy signal and the reference one are the same. The neural network(NN) ensemble filter not only well reduces additive and multiplicative white noise inside signals, but also preserves signals' characteristics. It is proved that while power of noise is larger, the reduction of noise using NN ensemble filter is better than the improved ¿ nonlinear filter and single NN filter, and compared with the improved ¿ nonlinear filter, degradation o the capability for reduction of noise by NN ensemble due to the increase of noise power is much suppressed. Furthermore, it is presented of the relationship between noise reduction and bandwidth of noises. The performance of the NN ensemble filter is demonstrated in computer simulations and actual electroencephalogram (EEG) signals processing.
authors.affiliation: Graduate School of Advanced Technology and Science, The University of Tokushima, Tokushima, Japan. cyj6226@ee.tokushima-u.ac.jp
authors.forename: Yongjian
authors.initials: Y
authors.lastname: Chen
authors.forename: Masatake
authors.initials: M
authors.lastname: Akutagawa
authors.forename: Masato
authors.initials: M
authors.lastname: Katayama
authors.forename: Qinyu
authors.initials: Q
authors.lastname: Zhang
authors.forename: Yohsuke
authors.initials: Y
authors.lastname: Kinouchi
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted