Performability of an interconnection
system depends upon the failure
characteristics of its components.
There is the need of a technique to
predict the performability of a
multiprocessing network from the
existing available input/output data.
In an interconnection network, the
processors are connected with each
other through links. There may be
imperfection at the links or at the
nodes, which affects the system
performance. Hence a general and
flexible prediction model needs to
be developed to compute the
reliability and performance of the
multiprocessor interconnection
networks. In this paper we presents
an artificial neural network model
based on principle of back
propagation algorithm to compute
the performability of crossed-cube
and star graph multiprocessor
interconnection networks.

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