Optimal discriminant feature-based waveform recognition with neural networks

We describe an optimal discriminant feature based pattern recognition system with two-layer feedforward neural networks. This system can simplify the structure of the covariance matrix to facilitate visual interpretation of cluster graphs and to assess clustering performance. It also can enable one class to be well concentrated and an other maximally spread over the space, while keeping the inter-cluster distance as large as possible. The performance of our 2-D optimal discriminant feature-based neural network system does not degrade compared with a multidimensional feature-based neural network system, while the computational complexity and the storage requirements are significantly improved. This technique has been applied and has shown accurate classification (98% in probability) of digital waveforms obtained from digitized radiation detector outputs. To the best of our knowledge, application of such a technique is novel