Investigating Neural Network Efficiency and Structure by Weight Investigation

This research investigates the analysis and efficiency of neural networks, using a technique for network link pruning. The technique is tested with inefficient architectures for the XOR problem and then for a network from a real world, complex, image recognition task. By removing each link and examining effect upon error level, a fuzzy set is developed with membership indicating link saliency. As well as efficiency, the technique is useful to investigate solution architecture. It is hypothesised that similar insights may be gained for any problem solved by similar architecture This paper begins with the background, research and possible applications. Experimental design, implementation, methodology and results are given. The conclusion considers implications and suggests further research. Results indicate that this technique can significantly improve efficiency of a neural network for a real application. Both memory requirements and execution speeds improve by nearly 30 times. Further development is hoped to deliver improvements to efficiency and depth of investigation.