The University of British Columbia

Significant performance improvements may be obtained in digital communication systems if error control coding is properly applied. However, selection of a coding scheme for specific applications is often a complicated task. The choice is affected by a set of system design goals. Some of these goals impose case-dependent conflicting requirements. Similar scheme selection problems exist in many engineering system design processes. A knowledge-combined neural network approach is developed and applied to optimum coding selection. The proposed approach utilizes a neural network trained not only by precedent examples but also by knowledge rules to draw conclusions. It is shown that artificial neural networks (ANNs) can provide effective solutions to the problems encountered in building systems that emulate a coding specialist's expertise