Title :
A possible genetic-algorithm based method for optimizing a class of ANN transfer functions
Author :
Beddoess, Michael P. ; Ward, Rabab K.
Author_Institution :
Dept. of Electr. & Comput. Eng., British Columbia Univ., Canada
Abstract :
This paper proposes a hybrid of two methods to determine the weight-constants in a class of artificial neuron networks, ANNs. The class of ANNs we are interested in are characterized by feed-forward processing elements. One of the methods is the genetic algorithm, GA; the other is "training through" back-propagation of the error, BPE. We expect our hybrid scheme to be faster than using BPE alone.
Keywords :
backpropagation; feedforward neural nets; genetic algorithms; linear predictive coding; transfer functions; ANN transfer function optimization; artificial neuron networks; back-propagation error; feedforward processing elements; genetic algorithm; hybrid method; linear prediction coder; training; weight-constants; Artificial neural networks; Backpropagation; Circuits; Error correction; Genetics; Neurons; Optimization methods; Resource management; Transfer functions; Weight measurement;
Conference_Titel :
Digital Signal Processing, 2002. DSP 2002. 2002 14th International Conference on
Print_ISBN :
0-7803-7503-3
DOI :
10.1109/ICDSP.2002.1028345
