Tuning of the propagation models and optimization of the node parameters is done based on a feedback loop.
These changes increase the accuracy, robustness, and utility of the model, particularly for preliminary orbit propagation, estimation, and optimization applications in which fast, reasonably accurate force models and sensitivities are desirable.
Based on this abstract specification, we defined an algorithm that uses fabrication-related constraint propagation and an optimization protocol to suggest a spatially optimized design for the proposed circuit.
Experiments compare mean field approximation, Onsager approximation, belief propagation and belief optimization.
Then following the UMDO solving process, research progress of each key step is separately surveyed and discussed, specifically including uncertainty modeling, uncertainty propagation and analysis, optimization under uncertainty, and UMDO procedure.
Since the novel design criterion relies on signal level predictions, it must be used together with propagation prediction models and optimization engines in network planning tools.
The metamodel is formulated to approximate the system response (metamodel output) with respect to both the design variables and the uncertain model parameters (augmented metamodel input), so that it can simultaneously support the uncertainty propagation and the design optimization, adopting a sequential approximate optimization (SAO) strategy for the latter.
In this paper, we examine an engaged social media node targeting strategy to facilitate message propagation, and propose an optimization scheme incorporating this strategy to determine the optimal sets of nodes to target with planning horizon length, source messaging capacity, social network characteristics and user behaviors considered.
It should be mentioned that back-propagation neural networks and optimization type neural networks can be modeled as SNNs, whereas Hopfield neural networks, bidirectional associative memory neural networks, and cellular neural networks can be modeled as LFNNs [6].
Then the discrete solutions are combined using loopy belief propagation, and refined using local optimization.
