The reduction of model complexity thus becomes extremely important for complex control system design and implementation.
These results indicate that we were working at the limit of model complexity given our sample size, and more complex models would require considerably larger data sets (in terms of number of species) to avoid problems in estimation and model comparisons.
The advantages of model selection methods include a firm logical and theoretical basis and elegant handling of model complexity, both of which make them attractive for analyzing complex evolutionary models for biological sequence data.
Analysis of model complexity (K).
Three techniques in increasing order of model complexity are discussed.
Structural non-identifiability is seen to pervade even at modest levels of model complexity.
The role of model complexity in safety analysis is also described.
Hastie et al.[4] define effective degrees of freedom and use it as the measure of model complexity.
For a different SAR system, they may be reviewed or removed at the expense of model complexity.
The increase of model complexity up to a certain degree usually makes a model more realistic and increases performance.
They demonstrate the performance of our method in presence of Gaussian additive noise in terms of model complexity error.
