Title :
Soft competitive learning in the extended differentiator network
Author :
Kia, Seyed Jalal ; Coghill, George
Author_Institution :
Dept. of Electr. & Electron. Eng., Auckland Univ., New Zealand
fDate :
27 Jun-2 Jul 1994
Abstract :
A two-layer neural network called an extended differentiator network (EDN), which combines unsupervised and supervised training, is presented The EDN uses a soft competitive learning method in the unsupervised layer followed by a supervised associative layer. The soft competitive learning in the EDN takes the activity of all the competing neurons into account by using a one-step lateral inhibition mechanism. The functionality of the network is tested on a vowel recognition task and a cluster analysis problem. The simulation results indicate an effective use of the competing neurons, resulting in a high recognition rate in a network with a simple configuration
Keywords :
feedforward neural nets; pattern recognition; speech recognition; unsupervised learning; cluster analysis; competing neuron activities; extended differentiator network; network configuration; network functionality; one-step lateral inhibition mechanism; recognition rate; simulation; soft competitive learning; supervised associative layer; supervised training; two-layer neural network; unsupervised training; vowel recognition; Computational modeling; Computer simulation; Context modeling; Intelligent networks; Learning systems; Neural networks; Neurons; Signal analysis; Speech recognition; Testing;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374264