The adaptation of the parameters of the adaptive filters and the classifier can be formulated as an optimization problem, which minimizes the error between the actual responses of the classifier and the desired outputs.
Most of these methods based on generating random values from uniform, normal, or Cauchy distributions or by generating different values from pre-defined parameter candidate pool besides use the previous experience (of generating better solutions) to guide the adaptation of these parameters [11, 15, 16, 17, 19, 45, 46, 47, 48, 49].
A wider use of SWM modules, therefore, requires the adaptation of geometric parameters of the feed spacer to the water source.
A specific learning algorithm, based on the adaptation of local parameters of the activation function, is derived.
Since 2000s, a huge number of papers have been published which are based on the adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline.
The adaptation of the parameters α i should in many cases be performed in time intervals of less than a millisecond.
However, once the performance begins to increase the adaptation of these parameters reduces and they stabilize indicating a consolidation of the performance.
We could hypothesize that these differences arise from the adaptation of molecular parameters, such as codon usage, polypeptide folding and resistance to inactivation by mammalian cell proteases.
It is achieved due to the joint benefits of the AADC, the ASA and the resampling as they allow the online adaptation of parameters ( and ) by exploiting the input signal local variations.
