In recent years, after a period of disillusion in the eld of neural processing and adaptive algorithms, neural networks have been reconsidered for solving complex technical tasks. The problem of neural network training is the presentation of input/output data showing an appropriate information content which represent a given problem. The training of a neural structure will de nitely lead to poor results if the relation between input and output signals shows no functional dependence but a pure stochastic behaviour. This paper is concerned with the identi cation of the most relevant input-output data pairs for neural networks, using the concept of mutual information. A general, quantitative method is demonstrated for identifying the most relevant points from the transient measured data of a combustion engine. In this context mutual information is employed for the problem of determining the 50 per cent energy conversion point solely from the combustion chamber pressure during one combustion cycle.
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