Qualitative information about the structure of a mapping can surely be of help in learning a mapping by a collection of input-output pairs. However, there are conditions in which time and some other constraints make guessing the only plausible means for interpreting data. In this paper, the problem of the plasma boundary reconstruction in 'Tokamak' nuclear fusion rectors is assessed. The problem is formulated as an inverse 'identification' problem and the mapping is derived by a properly generated database of simulated experiments. Real data coming from experiments are also available to validate both numerically generated data and extracted model. The identification problem is solved for two different databases by using neural networks and more conventional models. The introduction of techniques derived from soft computing is shown to improve the performance in various respects. Dynamic identification systems appear to be rather demanding also for such systems, for the need of rapidly interpreting real time data for discharge control. Soft computing approaches may yet yield some low cost ways to take decisions during plasma evolution. The approximate analysis of experimental data could also improve the knowledge on the particular problem allowing an evolution of the knowledge base. Experimental data related to ASDEX-Upgrade machine are presented in this work and preliminary processed. Soft computing techniques also allow to simply get ideas about two other interesting problems in plasma engineering, namely, the fault tolerance and the minimization of the number of sensors.