AI Article Synopsis
- The paper explores how artificial neural networks (ANNs) can be integrated into the Volterra series for detecting buried pipes.
- It frames the detection as a classification challenge, aiming to distinguish between surface noise and the actual reflection from a buried pipe.
- By correctly mapping a test site, the ANN is shown to model the system's transfer function, which is then used to derive a Volterra series representation for better detection and analysis.
Article Abstract
This paper discusses the embedding of artificial neural networks (ANNs) into the framework of the Volterra series for modelling the problem of detecting buried pipes. This problem is formulated as a classification task whereby it is necessary to discriminate between the ground surface and an actual pipe reflection buried in noise in the return signal from ground probing radar. The objective is to filter out the unwanted surface reflection to enable improved mapping of the site being surveyed. Since the ANN correctly maps out a real test site, it can be viewed as having modelled the system transfer function relating the training patterns to their respective classes. Using the weights learnt by the ANN and its nodal functions, this transfer function is mathematically formulated. It is shown that the latter leads to a Volterra series representation of the pipe detection problem and effectively lends itself to the extraction of the Volterra kernels for this particular system. Copyright 1996 Elsevier Science Ltd
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| http://dx.doi.org/10.1016/0893-6080(95)00083-6 | DOI Listing |
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