Butcher, John B (2012) Reservoir Computing with high non-linear separation and long-term memory for time-series data analysis. Doctoral thesis, Keele University.

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Left unchecked the degradation of reinforced concrete can result in the weakening of a structure and lead to both hazardous and costly problems throughout the built environment. In some cases failure to recognise the problem and apply appropriate remedies has already resulted in fatalities. The problem increases with the age of any structures and consequently has become more pressing throughout the latter half of the 20th century. It is therefore of paramount importance to assess and repair these structures using an accurate and cost-effective approach. ElectroMagnetic Anomaly Detection (EMAD) is one such approach where currently analysis is performed visually, which is undesirable. A relatively new Recurrent Artificial Neural Network (RANN) approach which overcomes problems which have prohibited the widespread use of RANNs, Reservoir Computing (RC), is investigated here.

This research aimed to automate the detection of defects within reinforced concrete using RC while gaining further insights into fundamental properties of an RC architecture when applied to real-world time-series datasets. As a product of these studies a novel RC architecture, Reservoir with Random Static Projections (R2SP), has been developed. R2SP helps to address what this research shows to be an antagonistic trade-off between a standard RC architecture’s ability to transform its input data onto a highly non-linear state space whilst at the same time possessing a short-term memory of its previous inputs.

The R2SP architecture provided a significant improvement in performance for each dataset investigated when compared to a standard RC approach as a result of overcoming the aforementioned trade-off. The implementation of an R2SP architecture is now planned to be incorporated on a new version of the EMAD data collection apparatus to give fast or near to real-time information about areas of potential problems in real-world concrete structures.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Reservoir Computing, Reservoir with random static projections, Electromagnetic anomaly detection, Reinforced Concrete, Non-linearity, Short-term memory, Time-series data
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Contributors: Haycock, P (Thesis advisor)
Day, CR (Thesis advisor)
Depositing User: Users 4 not found.
Date Deposited: 19 Nov 2015 12:23
Last Modified: 12 Aug 2022 11:42
URI: https://eprints.keele.ac.uk/id/eprint/1185

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