A neuro-expert system with a conflict-resolving meta-neural network

摘要

Neural networks and expert systems are two different approaches to classification problems. Neural networks, powerful for generalization, robust system behavior, and parallel processing, are weak in explaining their results, whereas expert systems suffer from the knowledge acquisition bottleneck. This paper combines these two approaches, using the Ternary Synaptic Weights algorithm, in order to obtain a system with the benefits of both expert systems and neural networks without their inherent disabilities. We also introduce the concept of Meta-Neural Networks for classification conflict resolution, which is utilized in the implementation of a neuao-expert system for project management.