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二维广义系统Roesser模型局部能达和局部能控性

ISSN号：1000-5781
期刊名称：《系统工程学报》
时间：0
分类：TP30[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]
作者机构：[1]Institute of Systems Engineering, Tianjin University, Tianjin 300072, China, [2]Department of Computer Science, Dezhou University, Dezhou 253023, China
相关基金：Supported by National Natural Science Foundation of China （No.70471049） and China Postdoctoral Science Foundation （No. 20060400704）. NING Yufu, born in 1967, male, Dr, associate Prof.

关键词：模糊变量, 模糊程序, 模糊算法, 神经网络, 近似理论, fuzzy variable, fuzzy programming, fuzzy simulation, neural network, approximation theory, perturbation techniques computer simulation simultaneous perturbation stochasticapproximation algorithm

中文摘要：

为了解决三种模糊编程，当模特儿，即模糊期望的价值模型，模糊抑制机会的编程模型，和模糊依赖机会的编程当模特儿，同时的不安随机的近似算法被与模糊模拟集成神经网络建议。起初，模糊模拟被用来产生一套输入产量数据。然后，一个神经网络根据集合被训练。最后，训练神经网络在同时的不安被嵌入随机的近似算法。同时的不安随机的近似算法被用来寻找最佳的答案。二个数字例子被举说明建议算法的有效性。

英文摘要：

In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value and fuzzy dependent-chance programming model, a simultaneous perturbation stochastic approximation algorithm is proposed by integrating neural network with fuzzy simulation. At first, fuzzy simulation is used to generate a set of input-output data. Then a neural network is trained according to the set. Finally, the trained neural network is embedded in simultaneous perturbation stochastic approximation algorithm. Simultaneous perturbation stochastic approximation algorithm is used to search the optimal solution. Two numerical examples are presented to illustrate the effectiveness of the proposed algorithm.

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