| Summary: | 碩士 === 國立交通大學 === 資訊工程研究所 === 82 === This research attempts to develop a neural network expert
system for the diagnosis of telecommunication switching
systems. The neural networks were trained with previous faulty
situations of switching systems and their human diagnostic
responses by means of a suitable learning algorithm. We have
developed a generalized neural networks system so that by
training with a fault diagnosis examples of a particular type
of switching system, then the neural networks can be used for
diagnosis on this type of switching system. By using binary-
value data and having rapid training speed, the Binary Adaptive
Networks (BAN) model and Adaptive learning algorithm are
selected for the neural network diagnosis system. In addition,
some modifications of learning algorithm, based on locally-
tuned property of BAN and simulated annealing principle, are
proposed to alleviate the local minimum on the network learning
problems. The effectiveness of the modifications is presented
in the experiments. From the simulation results, while the
node number of a BAN is 20000, the correct diagnosis rate of a
GTD-5 switching system is above 99% by using the modified
learning algorithm. Furthermore, the diagnosis rate on the one
bit variation of the training data is between 90% and 95%, and
when the variations of training data get up to five bits the
diagnosis rate drops between 70% and 90%. As the training data
are collected more and more, the diagnosis rate increases quite
well. We also have proposed the hardware implementation of BAN
by systolic array processor architecture and evaluated the VLSI
implementation feasibility of the BAN design.
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