DocumentCode
1816406
Title
A learning algorithm for multi-layer perceptron networks with nondifferentiable nonlinearities
Author
Buhrke, Eric R. ; LoCicero, Joseph L.
Author_Institution
Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA
Volume
1
fYear
1992
fDate
7-11 Jun 1992
Firstpage
944
Abstract
A learning algorithm is proposed for neural networks with hard limiting nonlinearities. The algorithm is gradient-based, where the gradient is related to the average network response rather than to its instantaneous value. This gradient is well defined and computable. The algorithm was demonstrated on a vowel discrimination problem, where good results were achieved
Keywords
feedforward neural nets; learning (artificial intelligence); speech recognition; learning algorithm; multi-layer perceptron; neural networks; nondifferentiable nonlinearities; vowel discrimination; Backpropagation algorithms; Computational efficiency; Feedforward neural networks; Information processing; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Pattern recognition; Quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-0559-0
Type
conf
DOI
10.1109/IJCNN.1992.287065
Filename
287065
Link To Document