Effect of Penalty Function Parameter in Objective Function of System Identification

The evaluation of an objective function for a particular model allows one to determine the optimality of a model structure with the aim of selecting an adequate model in system identification. Recently, an objective function was introduced that, besides evaluating predictive accuracy, includes a logarithmic penalty function to achieve a suitable balance between the former model’s characteristics and model parsimony. However, the parameter value in the penalty function was made arbitrarily. This paper presents a study on the effect of the penalty function parameter in model structure selection in system identification on a number of simulated models. The search was done using genetic algorithms. A representation of the sensitivity of the penalty function parameter value in model structure selection is given, along with a proposed mathematical function that defines it. A recommendation is made regarding how a suitable penalty function parameter value can be determined

Detection and quantification of naringenin and kaempferol in melastoma decemfidum extracts by GC-FID and GC-MS

Melastoma decemfidum is a plant species from the Melastomataceae family. The plant was reported to have bioactive flavonoids, which showed antioxidant and cytotoxic activities. Extracts from the leaves of 26 plants were made at room temperature with methanol. Detection and quantification of two of the flavonoids, namely naringenin and kaempferol, in the extracts were carried out by using gas chromatography-flame ion detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS). By optimizing the key experimental parameters, a linear response for the individual target compounds was obtained in the concentration range LOQ from 3.44 to 8.26 mu g mL(-1) (r(2) = 0.9731-0.9772), with LODs from 1.13 up to 2.72 mu g mL(-1) per 1.0 g of crushed leaves, and with repeatability within the relative standard deviation (RSD) of 1.65-1.81%