Robust parametric methods are worked out, and an heuristic initial tuning of the PID controller is proposed from additional pole-placement constraints on the rigid mode.
However, each environment often requires customized image processing tools relying on heuristic parameter tuning.
For classical locomotion analysis, each environment requires customized state-of-the-art image processing tools that rely on heuristic parameter tuning [ 69- 80].
In this article, we use a heuristic for tuning the initial scale of the bandwidth parameter h n : we first form a matrix (d ij ) with its entries d ij ≔(t i − t j ) (i={1,…, n}).
Since the problem of choosing these boolean bounding predicates is NP-complete, we implemented and tested several heuristics for tuning the bounding predicates on an index node, and several heuristics for deciding which nodes in the index to improve when available tuning time is limited.
A parallel hybrid framework that combines gene expression programming (GEP) as the evolutionary problem-solving methodology and alternative meta-heuristics for tuning parameter values of the parallel GEP runs is presented.
Lastly we propose a simulation optimization heuristic to tune system filters' (w) and (v) parameters.
The implementation of this framework is based on a client server architecture which includes clients that use GEP to evolve candidate solutions for the problem in question, and clients that use (possibly) other meta-heuristics to tune GEP input parameters.
A simulation-optimization heuristic for system tuning is described and allows easy adaptation to various facial expression analysis applications.
In order to show the effectiveness of the instruction branch tuning heuristics in reducing energy, we compared the results of TECH, TCAT, TECH-CYCLES, and TEMGA heuristics with optimal results.
