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This paper proposes a method to control vibration of arbitrary structures with spatially weighted objectives.
The general idea is to form a scalar optimization problem by multiplying each objective function with a positive weight and summing up the weighted objectives.
The method of weighted objectives has been applied as a method of optimization.
The optimization considers four weighted objectives and is conducted with the Nelder Mead Simplex method.
The presence of multiple objectives has been taken into account coupling the algorithm with a cooperative game theoretic approach and, for sake of comparison, with other methods, such as weighted objectives or Trade-off.
It is applied by means of a computer-based tool, which permits an ex-ante evaluation of alternative projects, rating them against several weighted objectives using a set of indicators, thus combining the common design practice of scenario planning with a multicriteria analysis.
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Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived.
Like the way we introduce weighting information into PV-DBOW, we can optimize the following weighted objective function: begin{aligned} sum ^{|T|}_{i=1}sum ^{|t_{i}|}_{j=1}mathrm{Weight}(w_{ij} mathrm{log}P(w_{ij}|w^mathrm{context}_{ij}) end{aligned} (5 An alternative of introducing weighting information into PV-DM is to change the representation of (w^mathrm{context}).
During the identification process, we are guided by a weighted objective function (performance index) in which a weight factor is introduced to achieve a sound balance between approximation and generalization capabilities of the resulting model.
By optimizing the above weighted objective, the conditional probabilities of words with large weights will have more proportions to the entire objective.
Moreover a new weighted objective function is proposed in the identification frame.
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