| 1. | Design and implementation of linear optimization solving system
大规模线性优化求解系统的设计与实现 |
| 2. | Nonlinear optimization is closer to the real situation than the simplified linear ones
非线性优化较简化的线性优化更接近实际情况。 |
| 3. | Especially , fuzzy linear optimize model has better excellence than ordinary linear optimize model
尤其是模糊线性优化模型有别于一般线性优化模型表现突出 |
| 4. | Traditional method can be classified two class : linear optimization technique and nonlinear optimization technique , linear optimization technique base on born approximation or rytov approximation is usually used to solve weak scattering problem
线性优化方法采用线性近似忽略了散射体内部的多次散射，可以有效的反演低对比度的问题，但对于高对比度问题的求解则有可能不收敛。 |
| 5. | Relative key concept and technique of this method was described in detail in this paper , such as the decomposition of balance matrix , the modes of self - stress , the modes of inextensional mechanisms , the analysis model of optimization under linear constrained condition , the integration of constrained matrix
文中对桁架理论的奇异值分解法、自应力模态、机构位移模态、有约束线性优化数学模型、约束矩阵集成等关键概念和技术进行了详细的推导说明。 |
| 6. | Aimed at the current problem of pipeline layout optimization technique , the research of irrigation pipeline layout and pipe diameter optimization has been done , the gis ( geography information system ) and graph theory were first put forwarded to applyed to the design of low pressure pipeline irrigation project in the paper . with the support of gis , the minimal spanning tree theory of graph theory and 120 project theory can be applied to irrigation pipeline ' s layout optimization . at the aspect of pipe diameter optimization , simplicial method and interior - point method are been used in solve liner optimization model of pipe diameter to reach minimum project cost or a nnual working cost of low pressure pipeline irrigation
本文主要针对当前南方地区低压管道输水灌溉规划设计中存在的技术难点，开发研究先进实用的树状低压输水灌溉管网计算机辅助设计系统。首次提出了将gis (地理信息系统)和图论技术应用于低压管道输水灌溉规划设计及灌溉管网优化中，在gis支持环境下，应用图论中的最小生成树法和120规划进行管道的最优化布置。建立以管道输水灌溉系统的年折算费用最小为目标函数的管径优化线性规划模型，并将内点法应用于线性优化模型的求解。 |
| 7. | A new method was given out in this paper , which is based on the singular value decomposition of the balance matrix of truss , followed by the method of optimization under linear constrained condition . an analysis program for this method was developed . the rightness o f t his method w as proven by the analysis and verify of engineering example
本文从桁架理论的奇异值分解出发，结合有约束线性优化方法，提出了解决这一问题的另一途径，编制了相应的分析程序，经过算例分析与验证，证明了该方法的正确性。 |
| 8. | It is firstly suggested by mu mu that nonlinear optimization method can be used to make sensitivity analysis of numerical model . but as one of nonlinear optimization problems in atmosphere and ocean science , it was very simply described in mu mu ' s paper and not verified by numerical experiment results
但是，作为大气和海洋研究中非线性优化问题的一个子问题，穆穆等在文章中对于用非线性优化方法对数值模式进行敏感性分析只作了简明扼要的介绍，所得到的分析结果没有通过数值试验结果进行验证。 |
| 9. | ( 3 ) reactive power optimal of radial network is a very complex nonlinear discrete optimal problem . to ensure the speediness , feasibility and optimality , evolution algorithm is improved by combination with interior point sequence linear optimal algorithm and principle of var balance . the improved algorithm is applied to the reactive power optimization and achieves distinct effect
( 3 )配网网络无功优化是一个非常复杂的非线性离散优化问题，为了保证计算结果的快速性、可行性和最优性，本文结合内点法的逐次线性优化方法，以及配网电压调节的特点，对模拟进化优化方法进行了改进，并将该算法应用于配网无功优化问题的求解，取得了显著的效果。 |
| 10. | 2 . on the base of detailedly analysing the fourier neural networks , we find this neural networks have the characteristic which can transform the nonlinear mapping into linear mapping . so , we improve the original learning algorithm based on nonlinear optimization and propose a novel learning algorithm based on linear optimization ( this dissertation adopts the least squares method ) . the novel learning algorithm highly improve convergence speed and avoid local minima problem . because of adopting the least squares method , when the training output samples were affected by white noise , this algorithm have good denoising function
在详细分析已有的傅立叶神经网络的基础上，发现傅立叶神经网络具有将非线性映射转化成线性映射的特点，基于这个特点，对该神经网络原有的基于非线性优化的学习算法进行了改进，提出了基于线性优化方法(本文采用最小二乘法)的学习算法，大大提高了神经网络的收敛速度并避免了局部极小问题；由于采用了最小二乘方法，当用来训练傅立叶神经网络的训练输出样本受白噪声影响时，本学习算法具有良好的降低噪声影响的功能。 |
