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Quantized kernel least mean square (QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance,...
Kernel least-mean mixed-norm (KLMMN) algorithm as a special kernel adaptive filter method achieves good performance when the measured noises are distributed with a linear combination of long-tails and short-tails. In order to reduce the computational efforts and improve the accuracy, this paper proposes a novel entropy optimized kernel learning algorithm, called E-KLMMN, on the basis of information...
A new fuzzy multi-objective decision-making model based on vague set theory and entropy method is presented in this paper. In the proposed model, the alternative schemes are weighted evaluation according to the integrated vague value of its elements relative to the ideal scheme. In order to calculate attributes weights objectively and accurately, a modified entropy weights calculation formula is proposed...
The report analyzes all the available experimental data from three laboratories on the entropy-energy relation of two different strongly interacting trapped Fermi gases, and compare this directly with a single universal theoretical prediction. A diagrammatic approach based on functional path-integrals is used together with the local density approximation to treat the inhomogeneous trap. Below the...
In complex multi-agent fusion systems resource conflicts are very likely to occur. We propose an algorithm that determines the optimal sensing resource to fusion task assignment, based on the entropy change criterion. By exploiting the Bayesian network framework and the structure of our agent network the algorithm operates in a distributed manner by combining descriptions of local fusion models in...
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