Title :
Application of neural nets to feature fusion
Author :
Brotherton, Tom ; Mears, Eric
Author_Institution :
Orincon Corp., San Diego, CA, USA
Abstract :
A multiple-feature-multiple-neural fusion solution to the problem of detecting and classifying transient signals, denoted as a hierarchical neural net, is described. The fusion of the multiple features leads to significant gain in both detection and classification performance while at the same time reducing false alarms. The multinet approach gives very good results even in cases where the first layer nets operating on single features fail. Results comparing the single feature nets and the hierarchical neural net approach processing real data are presented
Keywords :
neural nets; signal detection; transients; feature fusion; multiple-feature-multiple-neural fusion solution; neural nets; transient signals classification; transient signals detection; Acoustic sensors; Covariance matrix; Data mining; Eigenvalues and eigenfunctions; Fast Fourier transforms; Feature extraction; Fourier transforms; Neural networks; Performance gain; Sensor phenomena and characterization;
Conference_Titel :
Signals, Systems and Computers, 1992. 1992 Conference Record of The Twenty-Sixth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-3160-0
DOI :
10.1109/ACSSC.1992.269166
