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.