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An intergeneration projection genetic algorithm is employed to seek for Pareto optimum of the uncertain problem.
The uncertain problem is developed; considering uncertain parameters, the suggested dual-objective facility programming model is written.
In all these approaches, the uncertain optimization problem is replaced by a deterministic version, called the robust counterpart of the uncertain problem.
It is quite clear that the behavior toward an uncertain problem should be flexible and therefore the fuzzy-based approach is applied to deal with such problems [4].
In order to solve the uncertain problem, a neural network based on radial basis function (RBF) is used to estimate the unknown dynamics of the robot manipulator.
Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters.
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Simulation models have been widely used in uncertain problems.
Fuzzy set theory has been proven to be effective on such uncertain problems.
They presented a new formulation of stochastic programming method which is able to minimize the total mean and risk values of stochastic -based uncertain problems.
Modeling of phenomena based on conventional mathematical tools (e.g., differential equations) is not appropriate for dealing with ill-defined and uncertain problems.
In this circumstance, interval analysis shows a significant advantage of handling uncertain problems since only the upper and lower bounds of inputs and outputs are defined.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com