E. Gelenbe et al., LEARNING NEURAL NETWORKS FOR DETECTION AND CLASSIFICATION OF SYNCHRONOUS RECURRENT TRANSIENT SIGNALS, Signal processing, 64(3), 1998, pp. 233-247
This paper proposes a neural network solution to the classical signal
processing problem of detection of a synchronous recurrent transient s
ignal in noise. If a signal exists, it is assumed to be one of M known
signals which may sometimes occur (probabilistically) in successive i
ntervals. Several neural network configurations are applied to this pr
oblem and compared with each other and with the optimum adaptive seque
ntial detector. A novel efficient neural network detector is proposed
using an XOR-Tree configuration with learning. Tests with synthetic an
d real noise, show the excellent performance of this approach as compa
red to the optimum adaptive detector and to other neural network techn
iques. With real (non-white) noise obtained from sonar data, the XOR-T
ree network widely outperforms the likelihood ratio detector. We also
discuss the learning time complexity of the XOR-Tree network and compa
re it to that of standard three layer network architectures. (C) 1998
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