Fault tolerance and redundancy of neural nets for the classification of acoustic data

An investigation is made of the relation between the fault tolerance of a multilayer perceptron (MLP) and its redundancy as determined by the number of hidden-layer neurons (x). Damage was introduced by cutting connections. The application studied is the classification of coins according to their acoustic emissions after striking a hard object. Several MLPs were trained by backpropagation to discriminate acoustic emission data from 6 classes of coin. The nets had 259 input nodes, 6 output nodes, and x varying between 5 and 25. In addition, one single-layer network (x=0) was trained. Results show that the single-layer perceptron (SLP)-although able to classify the data with 100% accuracy under fault-free conditions-was far less damage-resistant than any of the MLPs