Title :
The application of Evolutionary Neural Network for bat echolocation calls recognition
Author :
Mirzaei, G. ; Majid, M.W. ; Jamali, M.M. ; Ross, J. ; Frizado, J. ; Gorsevski, P.V. ; Bingman, V.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Toledo, Toledo, OH, USA
fDate :
July 31 2011-Aug. 5 2011
Abstract :
An Evolutionary Neural Network (ENN) is developed to identify bats by their vocalization characteristics. This is in an effort to identify local bat species as a large number of bat fatalities near wind turbines have been reported. ENN is based on the Genetic Algorithm, which can be used for optimization of the weight selection of the neural network. We then compare ENN with different classification techniques. In the scope of bat call classification, ENN is a new technique that can be effectively used as a bat-call classifier. This research will help in developing mitigation techniques for reducing bat fatalities. The ENN algorithm is developed in MATLAB.
Keywords :
acoustic signal detection; echo; feedforward neural nets; genetic algorithms; identification; pattern classification; ENN; bat call classifier; bat echolocation call recognition; bat fatality; evolutionary neural network application; genetic algorithm; local bat species identification; mitigation technique; vocalization characteristics; weight selection optimization; wind turbine; Biological cells; Classification algorithms; Feature extraction; Neurons; Software; Support vector machines; Training;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033347
