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In fuzzy computation, the united solution of intervals may represent the fuzzy solution.
Fuzzy Computation is also applied to estimate wave parameters and control floating wave energy converter.
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For example, the proposed approach can be extended based on the principles of fuzzy computations.
The general setting is the Hermite-type interpolation, where the values and the slopes of the monotonic interpolators are given by appropriate parameters and the overall errors of the fuzzy computations can be controlled within a prefixed tolerance by eventually augmenting the total number of pieces (and of the parameters) by which the results are obtained.
Computational intelligence can be categorized into three main groups, namely, neural computation, evolutionary algorithms (EAs), and fuzzy computing.
The fuzzy reliability computation of single component is a fundamental problem.
We then use carrier selection, fuzzy interpolation computation, and ant-genetic algorithm techniques to select the appropriate learning paths and objects.
This and other wave parameters can be estimated by means of Computational Intelligence techniques (Neural, Fuzzy, and Evolutionary Computation).
Conventional computational systems that encode information in discrete zeros and ones would require many components and many operations to carry out this fuzzy AND NOT computation.
Based on these definitions, direct computation of fuzzy inverse matrix is done using fuzzy arithmetic and fuzzy equation system.
The vision system also includes a new fuzzy-multiresolution threshold computation method, which considers knowledge on the application and information on the illumination conditions to select an appropriate threshold for the segmentation of the object of interest.
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