A method is used for evaluating forecasting error of short-term wind power using a set of indices of the error distribution [10].
CCF between two forecast error series is defined by: rho_{i,j} left( a right) = frac{{Eleft( {left( {e_{i,t} - mu_{i} } right)left( {e_{j,t - a} - mu_{j} } right)} right)}}{{sqrt {h_{i} } sqrt {h_{j} } }} (3)where i and j are the indices of the error series.
Finally, based on the LDI model, a neural network-based nonlinear filter (NNBNF) is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality (LMI) constraints.
Based on mode-dependent Lyapunov functions, a novel insensitive reliable H∞ filter is constructed in the framework of linear matrix inequalities (LMIs) technique to guarantee the stability and optimize the expanded H∞ performance index of the filtering error system.
The novelty is to design a set of full order filters and a switching rule to orchestrate them in order to assure an H2 performance index of the estimation error.
Moreover, a sufficient condition for the existence of the desired filter which guarantees the stochastic admissibility and the H∞ performance index of the resulting filtering error system is presented.
In view of the above, simplified indices of the measurement error level could be used, as proposed in our study for operations characterised by lower (thinning operations) and higher (clear-cut operations) work variability.
For the jth observation in the ith square, there is an observed value y ij and a predicted value ( {widehat{y}}_{ij} ) of the productivity index (300 Index or Site Index) with the error of prediction being ( {e}_{ij}={y}_{ij}-{hat{y}}_{ij} ).
Specifically, in GARP, we retained only the 20% of models that showed lowest omission errors, and then retained only the central 50% of the frequency distribution of proportional area predicted present (an index of commission error); the result was 10 'best subsets' models (binary raster data layers) that were summed to produce a best ensemble estimate of geographic projection.
By using an information theoretical approach, we have developed an index of the concentration of errors, i.e. the metric content index.
