Conclusion Fuzzy relative entropy method is more significant for fuzzy pattern recognition.

结论模糊相对熵方法用于模式识别是很有实际意义的.

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This paper propose an entropy - based Pareto evolutionary algorithm ( EPEA ) for multi - objective optimization.

提出了一种求解 多目标 优化问题的基于相对熵的 Pareto 解演化算法,首先分析了多目标优化中各目标间的补偿模式和非补偿模式,以及它们对应的Pareto解演化算法和经典加权求和算法.

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Aim In order to study the application of fuzzy relative entropy for fuzzy pattern recognition.

摘要目的研究模糊相对熵在模式识别中的应用.

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Methods of documentation, minimum relative information entropy method, and 3 scale AHP were employed.

研究方法:文献综述法 、 最小相对熵方法和三标度AHP方法.

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Approach of fuzzy enhancement based on relative entropy is proposed in this paper.

设计了一种新的基于相对熵的图像模糊增强方法.

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