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Applying the Computational Intelligence Paradigm to Nuclear Power Plant Operation: A Review (1990-2015).

Tatiana Tambouratzis T., Giannatsis J., Kyriazis A., and Siotropos P.


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    <dct:description>&lt;p&gt;In the guise of artificial neural networks (ANNs), genetic/evolutionary computation algorithms&lt;br&gt; (GAs/ECAs), fuzzy logic (FL) inference systems (FLIS) and their variants as well as combinations,&lt;br&gt; the computational intelligence (CI) paradigm has been applied to nuclear energy (NE) since the&lt;br&gt; late 1980s as a set of efficient and accurate, non-parametric, robust-to-noise as well as to-missinginformation,&lt;br&gt; non-invasive on-line tools for monitoring, predicting and overall controlling nuclear&lt;br&gt; (power) plant (N(P)P) operation. Since then, the resulting CI-based implementations have afforded&lt;br&gt; increasingly reliable as well as robust performance, demonstrating their potential as either stand-alone&lt;br&gt; tools, or - whenever more advantageous - combined with each other as well as with traditional signal&lt;br&gt; processing techniques. The present review is focused upon the application of CI methodologies to&lt;br&gt; the - generally acknowledged as - key-issues of N(P)P operation, namely: control, diagnostics and&lt;br&gt; fault detection, monitoring, N(P)P operations, proliferation and resistance applications, sensor and&lt;br&gt; component reliability, spectroscopy, fusion supporting operations, as these have been reported in the&lt;br&gt; relevant primary literature for the period 1990-2015. At one end, 1990 constitutes the beginning of&lt;br&gt; the actual implementation of innovative, and &amp;ndash; at the same time &amp;ndash; robust as well as practical, directly&lt;br&gt; implementable in H/W, CI-based solutions/tools which have proved to be significantly superior to&lt;br&gt; the traditional as well as the artificial-intelligence-(AI)derived methodologies in terms of operation&lt;br&gt; efficiency as well as robustness-to-noise and/or otherwise distorted/missing information. At the&lt;br&gt; other end, 2015 marks a paradigm shift in terms of the emergent (and, swiftly, ubiquitous) use of&lt;br&gt; deep neural networks (DNNs) over existing ANN architectures and FL problem representations, thus&lt;br&gt; dovetailing the increasing requirements of the era of complex - as well as Big - Data and forever&lt;br&gt; changing the means of ANN/neuro-fuzzy construction and application/performance. By exposing&lt;br&gt; the prevalent CI-based tools for each key-issue of N(P)P operation, overall as well as over time for&lt;br&gt; the given 1990-2015 period, the applicability and optimal use of CI tools to NE problems is revealed,&lt;br&gt; thus providing the necessary know-how concerning crucial decisions that need to be made for the&lt;br&gt; increasingly efficient as well as safe exploitation of NE.&lt;/p&gt;</dct:description>
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