Title :
Preprocessing of the input vectors for the linear associator neural networks
Author :
Haque, Abul L. ; Cheung, John Y.
Author_Institution :
Sch. of Comput. Sci., Oklahoma Univ., Norman, OK, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
This paper presents a methodology for ensuring the input to the linear associator neural network to be all linearly independent. This is a required condition for the linear associator neural network in order to produce exact output during recall. A number of linear associators may be connected in parallel to increase the capacity. A method to group all the input vectors as a set of linearly independent vectors is presented. The performance of the model is discussed
Keywords :
neural nets; input vector preprocessing; linear associator neural networks; Computer science; Data preprocessing; Neural networks; Noise measurement; Signal to noise ratio; Vectors;
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.1994.374305
