在此基础上，得到了向量目标函数既是似凸又是拟凸的多目标最优化问题的G-恰当有效解集是连通的结论。
On the conditions that vector objective function is like-convex and quasi-convex, we obtain the connectedness of G-proper efficient solution set of the multiobjective optimization problem.
提出了目标向量的简单变换方法及便于网络参数选择的收敛评价函数。
The simple transformation method for target vectors and criterion function convenient for selection of the parameters about the network has been put forward.
提出了目标向量的简单变换方法及便于网络参数选择的收敛评价函数。
The simple transformation method for target vectors and criterion function convenient for selection of the parameters about the networks has been put foward.
讨论了线性规划模型中，消耗系数矩阵a中某个基变量或某个约束方程的系数向量变化以及增减约束方程时，对最优基、最优解、目标函数值和影子价格的影响。
The paper discuss the effect exerting on objective function, optimum solution and shadow price in LP when consumption coefficient matrix a change and a inequality constraint is increased or decreased.
当线性规划约束条件的右端向量在一定范围内变化时，目标函数的最优值是右端向量的一个复杂的分片线性函数，但通常难以给出分析表达式。
The optimal objective value is a complicated piecewise linear function of the right-hand-side vector of the constraints, and its analytical expression is normally hard to obtain.
主要研究含矩阵函数半定约束和向量函数等式约束以及多个目标函数的多目标半定规划的对偶和鞍点问题。
The paper studied the multiobjective semidefinite programming with a semidefinite constraint of a matrix function and a multiobjective function.
PSO的位置向量对应模糊神经网络的权值向量，而PSO的适应函数对应模糊神经网络的目标函数，然后，通过演化PSO达到训练模糊神经网络的目的。
Position vector of a PSO is wight vector of trained FNN, and fitness function of the PSO is object function of trained FNN, the FNN is then trained by evolving the PSO.
提出了一个新的支持向量机模型——基于边界调节的支持向量机，并利用拉格朗日定理得到了这种支持向量机的对偶目标函数。
In order for an SVM to be more robust to noise, a new SVM model i. e., the support vector machine based on adjustive boundary SVMAB is proposed.
提出了一个新的支持向量机模型——基于边界调节的支持向量机，并利用拉格朗日定理得到了这种支持向量机的对偶目标函数。
In order for an SVM to be more robust to noise, a new SVM model i. e., the support vector machine based on adjustive boundary SVMAB is proposed.
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