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common structures for the well-known back-propagation algorithm [20]. Two
layered feedback structure, on the other hand, has been used by Carpenter and
Grossberg [3] for realization of adaptive resonance theory [4] and Kosko [13]-
[14] for realization of bi-directional associative memory. The last class of
topology shown in fig. 14.4(e) represents a recurrent net with feedback. Many
cognitive nets [12] employ such topologies. Another interesting class of
network topologies, where each node is connected to all other nodes bidirectionally
and there is no direct self-loop from a node to itself, has been
used by Hopfield in his studies. We do not show the figure for this topology,
as the readers by this time can draw it themselves for their satisfaction.
14.4 Learning Using Neural Nets
Artificial neural nets have been successfully used for recognizing objects from
their feature patterns. For classification of patterns, the neural networks should
be trained prior to the phase of recognition process. The process of training a
neural net can be broadly classified into three typical categories, namely,
Input
Feature
• Supervised learning
• Unsupervised learning
• Reinforcement learning.
Neural Net
Weight/ threshold
adjustment
Supervised Learning Algorithm
Fig. 14.5: The supervised learning process.
14.4.1 Supervised Learning
Error
Vector
Target
Feature
The supervised learning process (vide fig. 14.5) requires a trainer that submits
both the input and the target patterns for the objects to get recognized. For
example, to classify objects into "ball", "skull", and "apple", one has to submit
the features like average curvature, the ratio of the largest solid diameter to its