Context : Nanoflares are small impulsive bursts of energy that blend with and possibly make up much of the solar background emission .
Determining their frequency and energy input is central to understanding the heating of the solar corona .
One method is to extrapolate the energy frequency distribution of larger individually observed flares to lower energies .
Only if the power law exponent is greater than 2 is it considered possible that nanoflares contribute significantly to the energy input .

Aims : Time sequences of ultraviolet line radiances observed in the corona of an active region are modelled with the aim of determining the power law exponent of the nanoflare energy distribution .

Methods : A simple nanoflare model based on three key parameters ( the flare rate , the flare duration , and the power law exponent of the flare energy frequency distribution ) is used to simulate emission line radiances from the ions Fe \scriptstyle { XIX } , Ca \scriptstyle { XIII } , and Si iii , observed by SUMER in the corona of an active region as it rotates around the east limb of the Sun .
Light curve pattern recognition by an Artificial Neural Network ( ANN ) scheme is used to determine the values .

Results : The power law exponents , \alpha \approx 2.8 , 2.8 , and 2.6 are obtained for Fe \scriptstyle { XIX } , Ca \scriptstyle { XIII } , and Si iii respectively .

Conclusions : The light curve simulations imply a power law exponent greater than the critical value of 2 for all ion species .
This implies that if the energy of flare-like events is extrapolated to low energies , nanoflares could provide a significant contribution to the heating of active region coronae .