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An improved code selection algorithm for fault prediction

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Abstract

The goal of this study is to present an improved code selection algorithm (BCSA) for fault prediction. The contributions mainly contain three parts. The first part is on the extension of the horizontal input in the code selection algorithm (CSA). We propose that the horizontal input is also the prediction for the next coming event, not only for recalling. Thus, BCSA is able to recall and predict alternately. The second part is on the extension of the generic minicolumnar function. We propose that the function of a minicolumn is to be a k-winner-take-all competitive module (CM) and all active cells (the overall input is 1) should be chosen as winners within a CM. The third part is on the improvement of the competition mechanism. In BCSA, the winners are directly chosen with only one round competition. Thus, computing the input’s similarity G is unnecessary. BCSA is applied to analyze the disaster of the space shuttle Challenger which is a well-known example of fault prediction. Compared to other methods, the result of BCSA is specific, robust and independent of the parameters.

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Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of China

    Yin Kuang, Zhang Yi & Lei Zhang

Authors
  1. Yin Kuang
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  2. Zhang Yi
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  3. Lei Zhang
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Corresponding author

Correspondence to Zhang Yi.

Additional information

This work was supported by National Basic Research Program of China (973 Program) under Grant 2011CB302201 and National Nature Science Foundation of China under grant No. 60931160441.

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Kuang, Y., Yi, Z. & Zhang, L. An improved code selection algorithm for fault prediction. Neural Comput & Applic 22, 1763–1772 (2013). https://doi.org/10.1007/s00521-012-1203-z

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