S. Rajasekaran et Gav. Pai, TRAINING FREE COUNTERPROPAGATION NETWORKS AS STATIC HETEROASSOCIATIVEMEMORIES, INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES, 4(6), 1997, pp. 245-253
Counterpropagation networks (CPN) that belong to the category of self
organization networks, function as statistically optimal self programm
ing look up table. However, the weight adjustment criterion between th
e input and the competitive layer that follows Kohonen's unsupervised
learning rule, and the same between the competition and the interpolat
ion layer which follows Grossberg's supervised learning rule, can be d
ispensed with, without affecting the learning ability of the network.
Such a network, termed as Training free counterpropagation network (TF
CPN) is computationally efficient. Also, associative memories which ar
e a class of neural networks, are content addressable and possess the
capability to store a large set of patterns as memories. In this paper
, the TFCPN scheme and its behaviour as a static hetero-associative me
mory model is discussed. The TFCPN model has been successfully applied
to an example in pattern classification and in the design of fink tru
ss in the field of structural engineering.