We present a review of the pattern recognition approach in order to study its ability to solve the diagnosis problem of technological systems. We first state the problem and highlight the link between pattern recognition and diagnosis. Many theoretical and practical factors need to be considered. We then describe a common adaptive diagnosis procedure with emphasis on the design and real time operation. Finally, we conclude with directions for future work to solve this problem. >

http://ieeexplore.ieee.org/iel2/3093/8853/00390863.pdf

Pattern recognition for diagnosis of technological systems: a review

After a general presentation of the problem of unsupervised learning for diagnosis by pattern recognition, the methodology proposed is described as well as the necessary mathematical tools. This methodology is illustrated by the steam generator of a nuclear power plant.

Unsupervised learning methodology: Application to nuclear power plants

The nonparametric kernel classification rule derived from incomplete data is studied. Methods of designing kernel decision rules possessing optimal asymptotic properties are proposed. Consistency and rates of convergence are examined. It is argued that the replacement methods using the regression approach can lead to the inconsistency of resulting decision rules. On the other hand, a method employing the concept of predictive density yields asymptotically optimal classification rules. >

Kernel classification rules in the presence of missing values

Estimation of missing streamflow data using principles of chaos theory

This paper deals with incomplete data sets. This subject is analyzed and some methods for estimating missing values are presented. Furthermore, the results of a simulation experiment are presented.

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