Abstract
This article shows the model of mobile asynchronous transfer mode (ATM) system, and it proposes neural error control coding in this system. A knowledge-combined neural network (NN) approach is developed and applied to optimum coding selection. The proposed approach utilises a NN trained not only by precedent examples but also by knowledge rules to draw conclusions. It is shown that an artificial NN can provide effective solutions to the problems encountered in mobile ATM system that emulate a coding specialist's expertise. The major issue discussed is the effective application of NN techniques to optimum selection of error control coding schemes for mobile ATM systems.
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