Classification and verification through the combination of the multi-layer perceptron and auto-association neural networks

The multi-layer perceptron (MLP) classifier has excellent discriminatory properties but forms open decision boundaries, which makes it inappropriate for detecting nonclass data. The auto-association neural network (AANN), on the other hand, creates closed decision boundaries around the training set and is thus appropriate for detection and verification in the absence of counter-examples. However, we illustrate that AANNs may fall short in discriminating between classes that lie close to each other or are overlapping in feature space. To overcome each of the network types´ weaknesses, we propose a combined system consisting of one MLP and C AANNs for C-class recognition problems. Experimental results show that we can maintain good discriminatory properties whilst reliably detecting non-class data. This is illustrated in the context of radio communication signal recognition.