Organisation for Economic Co-operation and Development
Nuclear Energy Agency
Nuclear Science Committee


The neural networks are getting widespread application almost in all engineering fields as the researches are reported in the literature. Perhaps the most important feature they exibit is that they can efficiently process data where parallel processing is desirably required due to size and complexity of the data. Among their outstanding appealing properties, often their ability to process multivariate data with nonlinear as well as linear dependence among the variables and their fault-tolerance are referred.

Although the reported works with neural networks indicate the recognition of this new technology due to the successful outcomes, the degree of success and the limitations at the same time involved are neither clearly reported nor well understood. This is due to complex information processing mechanism of neural networks in the multidimensional space so that the detailed modelling of the mechanism has not been identified yet, in spite of focused attention on this issue. As result of this, often the outcomes of the applications reflected the effectiveness of the neural network methodology rather than the engineering predictions before the application at hand, as the latter is the case in most other engineering applications as it should be.

On the above described perspective, the present benchmark is designed to identify the effective utilization of neural network technology in nuclear engineering field to investigate the possibilities to improve the existing methodologies in use. In this respect plant monitoring is selected as the application area and participants are requested to report their way of neural network utilization using the data distributed and their reasonings for their approach yielding their neural network structure in use.

Thus, in brief the goal of the benchmark can be stated as follows: The benchmark is intended to identify the applicability of the neural networks in the nuclear industry for monitoring.


Data are obtained from a fuel cycle operation and correspond to different operations. This includes start-up (I), normal operation (II), shutdown (III), start-up (IV), normal operation (V), and again shutdown (VI) relevant to the end of the fuel cycle. Identification of the signals are given in Table 1 and locations of the sensors are given in Fig. 1.

Data file comprise 44 variety of signals, 32 of which are given in each of 6 particular operations. Data is formed by combining these six operations sequentially so that the whole data consist of 1169 time steps where at each time step 32 signals are given.

For neural network utilization at each time step a suitable number of signals are selected which are subject to analysis. Therefore each time step is used to form a pattern, the total number of patterns being maximum 1169. Operations and related information are given in Table 2, where actual 32 channel identification is given for each operation. In the first operation time, step is selected as 10 minutes and in the following operations time steps are 1, 2, 1, 1 and 1 minute respectively.

Data are given in ASCII format on a floppy suitable for PC-reading. A sample printout of the starting part of the file is given in Fig. 2 for 7 time steps.

The full data is available here


Benchmark tasks are divided into two parts. The first part contains the basic tasks which are requested to be carried out as a minimum to participate in the Benchmark.

In the whole benchmark analysis 14 signals will be used ( see Fig.3), which are shown as bold in Table I and Table II (channels: 1, 7, 8, 14, 15, 16, 17, 18, 19, 24, 25, 26, 27, 29). It is important to note that the temperature information at the input and the output of the core will be used in differential form; that is the neural network will receive the information as difference between channel 15 and channel 14 as well as difference between channel 17 and channel 16 (see on figures 4 and 5).

Second part of the benchmark contains optional tasks which are required to be performed in a similar way as before with some additional analyses, so as to reflect the outcomes of your further studies.

As the data is obtained from a real process the relative measurement errors in signals is approximately the same for all signals and it is less than 1 % for nominal operational conditions.

A. Primary (basic) Tasks

Task a Use first 600 patterns from the beginning for training your neural network and perform recall (i.e. neural network estimation) for both 942 patterns and total 1169 patterns. The neural networks structures to be used in their analysis are given in Fig 4 (11 inputs, 1 output) and Fig. 5 (autoassociative network with 12 input and 12 outputs).
Task b Carry out the same analysis in Tasks a using 1000 patterns in learning. In this case the recall is requested only for total 1169 patterns for both network structures.
Task c Instead of taking 8 nodes in the hidden layer use another number instead of eight if eight for any reason is concluded to be not appropriate and/or optimal.

Reporting the results and complementary questions

  1. Report in files (ASCII on a floppy) the estimated values by neural network for 942 and 1169 patterns seperately together with the respective errors (difference in physical units) for the same number of patterns.

  2. Report in a file the estimated values of Task b for 1169 patterns together with respective errors as defined above for the same number of patterns.

  3. Report in a file the weights of each neural structure used after the respective training processes. This implies, for instance, in Fig. 5 reporting the followings:

    WI(L,M)= input weights; L is the number of input (L=12), M is the number of nodes in the hiddenlayer (M=8)
    WTI(M)= bias in the sigmoid function at hidden layer (M=8)
    WTO(N)= bias in the sigmoid function at output (N=12).

  4. Give sum of the squared errors averaged over the number of patterns used during the training.
Also, give enough information (e.g.) normalization (if it is used at all) of the input and the output data etc, about the introduction of the data to the neural network so as to be able to reproduce the results at hand during later evalution of the reported results.

B. Secondary (optional) Tasks

Task d Carry out sensitivity analysis for cases where neural network is trained in autoassociative mode. Sensitivity is defined as the variation of each output with respect to the inputs. It is not ment for the variation of the synaptic weights.
Task e The data delivered contained redundant signals so that one might consider it is worth to repeat the benchmark experiments in Task a and Task b once more including these redundant signals.

Reporting the results and complementary questions

  1. Interpret the results of Task d and explain the importance and robustness of this analysis.

  2. Report the results of Task e in the form as described in Task c.

Final notes:

Organizers: E. Türkcan (ECN) and Ö. Ciftcioglu (ITU)


E. Türkcan
Netherlands Energy Research Foundation ECN
Nuclear Energy, Dynamic Signal Analysis
P.O.Box 1, 1755 ZG Petten
The Netherlands

E-MAIL : turkcan@ecn.nl
Telephone: (+31) 2246-4385 / 4262.
Telefax: (+31) 2246-3490