参数最优解

词组短语

parametric optimal solution

与参数最优解相关的网络例句 [注：此内容来源于网络，仅供参考]

PSO is a population-based optimization algorithm, which mimics the social behavior of animals in a flock. It makes use of individual and group memory to update each particle position allowing global and local search optimization. The objective function considered was the total weight of the structure subjected to stresses, displacements and forces constraints. The effects of the parameters were investigated as well and such combination of tuning parameters promote a better global search behavior avoiding premature convergence while rapidly converging to the optimal solution. Results showed the effectiveness of the proposed method by comparing with ANSYS Design Optimization Tool (zero-order method). The PSO with the tuning parameters makes it an ideal method for offshore wind turbines foundations optimization tasks.(2) A reliability analysis method for pile foundation bearing axial loads based PSOThe performance function of pile foundation's axially bearing capacity sometimes is nonlinear and complex, on the basis of geometric meaning of structural reliability index, an optimum model with PSO for structural reliability analysis under arbitrary random variables was established, The PSO algorithm is very efficient to solve global optimization problemsIts use in structural reliability field presents not only the advantage of its facility of implementation, but also the possibility to obtain the design point and the failure probability with a good accuracy. In addition, PSO is a zero order algorithm, for no derivative is necessary for its implementation.

本文的研究针对桩式海上风机基础结构的特点，在国内外有关研究成果的基础上对海上风机基础结构优化设计理论和可靠度方法一些相关问题进行了较为深入的研究，具体做了以下几个方面的工作：(1)基于粒子群优化的桩式海上风机基础确定性优化设计方法桩式海上风机基础的优化设计是一个复杂的、非线性约束的优化问题，针对传统的基于梯度信息的优化方法在处理非线性问题中易陷入局部最优解的问题，本文将一种耦合惩罚函数的PSO算法引入到海上风机基础结构概念设计中，PSO算法是从群体动物聚集觅食这一活动中受到启发而发展的，该算法利用个体和群体的信息共享不断改进自身的位置从而进行局部和全局最优搜索，本文中以桩和三脚架连接段直径及壁厚为设计变量，以基础总重量作为优化的目标函数，在给定的约束条件下建立了三脚架基础优化数学模型，另外本文还研究了PSO参数变化对结果的影响，协调的参数组合可以避免陷入早熟收敛而能够快速的获得全局的最优解，通过与ANSYS优化模块的计算结果比较验证了该方法的有效性，本方法为海上风机基础的确定性设计提供了一条有效的途径。

Moreover, by introducing memory function and vaccine inoculation mechanism of immune system, at the same time, DECA can converge to the optimal solution rapidly and stably.

通过改进的遗传策略来优化染色体长度，实现对聚类个数进行全局寻优，利用FCM算法加快聚类中心参数的收敛；并引入免疫系统的记忆功能和疫苗接种机理，使算法能快速稳定地收敛到最优解。

Based on genetic algorithm,a new hybrid coding method using the integer and floating number was presented. The enumerable method guarantees each individual in the population to satisfy the constrains, thus the punishment function was avoided. The two adjustable parameters made the evolution process jump out of the local best solution perfectly.The application of the fitness assignment based on Pareto ranking attained inferior solution set of the multi-objective and multiple options were provided to the decision-maker.

设计了一种新的整数和浮点数结合的编码方式；通过列举法使得每代个体满足约束条件，避免了罚函数的使用；可调整的双参数变异算子使进化过程能够较好地跳出局部最优解；应用基于Pareto排序的适应值分配方式得到目标函数的非劣解集，为决策者提供了多种选择方式。

They are as following: to establish the selection method of the slaving designed quality functions according to the interrelationship among designed quality functions; to construct the measurement functions of the optimality robustness with the hypersurface characteristics around the current iterative point of designed quality functions.

具体地，根据设计性能函数之间相互关系给出了处于支配地位设计性能函数的选择方法；利用设计性能目标函数在当前迭代点附近的超曲面特征，构造设计性能目标函数的最优性健壮度量函数；按照系统有序化设计模型的最优解应同时具有可行性健壮与最优性健壮的要求，通过计算设计性能目标函数与约束函数对设计变量的波动与非设计参数的波动的一阶敏度来确定系统有序化设计模型中的健壮参数；并以此为基础建立了进一步考虑非设计参数波动对设计变量波动有影响的后健壮分析方法。

Furthermore, buckling effect factors and their influence law are exploited. Finally, combining the character of piles foundation with high bridge piers with engineering practice, four optimal mathematic models are proposed and flow diagram of optimization analysis is fulfilled by the computer optimum program. A numerical example is analyzed to get optimal solution by using this program. Meanwhile based on the optimal model with buckling coefficient as the objective function, parameter variables are discussed about their effect rule toward the optimal value of objective function.

最后，针对高桥墩桩基础的特点，在基本优化模型的基础上，根据不同的目标函数建立了相应的优化模型，给出优化设计算法流程图，编制了相应的优化计算程序；并根据高桥墩桩基的工程实际设计了一个数值算例，采用本文编制的优化计算程序对该结构进行优化，得到了优化后的最优解；同时，采用高桥墩桩基屈曲系数作为目标函数的优化模型进行因素分析，初步探讨了参数变量对目标函数最优值的影响规律和程度。

Conceptually, we present the principle of optimality for defuzzification and the optimal defuzzification mapping; methodologically, we convert the problem of finding the optimal defuzzification mapping into the one of finding the optimal parameters, suggesting a specific method for its realization—POD method; operationally, we advance that the learning of the parameters for defuzzification can be realized by the learning process using extended Kalman filter.

在概念上，提出了解模糊的优化原理和最优解模糊映射；在方法上，将求最优解模糊映射问题转化为一个参数优化问题，并提出了一个具体实现方法——POD方法；在实际操作上，我们提出可用基于增广Kalman滤波的参数学习算法来完成解模糊过程参数的学习。

This paper presents a method to embed the original problem into separable parametric optimization problems and proves that the optimal solutions of the original problems are in the set of solutions of separable parametric optimization problems.

本文把原问题嵌入到可分的参数规划问题中，并证明了原问题的最优解包含在可分的参数规划问题的最优解集中。然后从最优解集中挑出原问题的最优解。

Optimization; parametric quadratic convex programming; set-valued map; directional derivative; linear stability; solution-set map; parametric linear programming; error bound; subdifferential map; lower locally directionally Lipschitzian; upper locally di-rectionally Lipschitzian; locally directionally Lipschitzian; convex function; quasidiferential; kernelled quasidiferential; quasi-kernel; star-kernel; star-diferential; Penot diferential; subderivative; superderivative; epiderivative; set-valued optimization; set-valued analysis; subdifferential; optimization condition;ε-dual; scalization; generalized subconvexlike-cone;ε-Lagrange multiplier

基础科学，数学，运筹学最优化；集值映射；方向导数；线性稳定；最优解集映射；参数线性规划；参数凸二次规划；误差界；次微分映射；下局部方向Lipschitzian；上局部方向Lipschitzian；局部方向Lipschitzian；凸函数；拟微分；核拟微分；拟核；星核；星微分； Penot-微分；上导数；下导数； Epi-导数；集值优化；集值分析；集值映射的次微分；最优性条件；广义锥次类凸；ε-对偶；数乘；ε-Lagrange乘子

In this method, a global approximately optimal solution of all ANFIS parameters is obtained using genetic algorithm and then premise parameters and consequent parameters are fine tined respectively using BP algorithm and Least Squares Estimate.

这种新混合学习方法首先利用遗传算法得到ANFIS所有参数的一个全局近似最优解，然后再利用BP算法和最小二乘法分别对前提参数和结论参数进行细化调整。

However, we can not get the film parameters directly because the ellipsometer equation is a transcendental equation. We can hardly get the analytical solution for film parameters from the measured ellipsometric parameters Ψ and Δ. Therefore, to find an algorithm to inverse the measured data Ψ and Δ becomes a primary but important problem.

但是通过椭圆偏振测量只能得到椭偏参数Ψ和Δ，由椭偏参数求解薄膜结构参数的椭偏方程是一个超越方程，很难得到精确的解析解，因此一般采用数值反演迭代不断逼近测量数据，将最优解作为测量结果。

与参数最优解相关的网络解释 [注：此内容来源于网络，仅供参考]

parametric hypothesis：参数假设

parametric estimation 参数估计 | parametric hypothesis 参数假设 | parametric optimal solution 参数最优解

parametric hypothesis：参数假设无忧雅思网

parametric estimation 参数估计无忧雅思网3H/i:q1?~W | parametric hypothesis 参数假设无忧雅思网2i4f | parametric optimal solution 参数最优解无忧雅思网"rX5L RaQg

parametric optimal solution：参数最优解

parametric hypothesis 参数假设 | parametric optimal solution 参数最优解 | parametric optimization 参数最优化

parametric optimal solution：参数最优解无忧雅思网

parametric hypothesis 参数假设无忧雅思网2i4f | parametric optimal solution 参数最优解无忧雅思网"rX5L RaQg | parametric optimization 参数最优化无忧雅思网*}"w%[*p8pPSl9Xt

parametric optimization：参数最优化

parametric optimal solution 参数最优解 | parametric optimization 参数最优化 | parametric programming 参数规划

parametric optimization：参数最优化无忧雅思网

parametric optimal solution 参数最优解无忧雅思网"rX5L RaQg | parametric optimization 参数最优化无忧雅思网*}"w%[*p8pPSl9Xt | parametric programming 参数规划无忧雅思网Y&e D_P(r:?