| 1. | ( 4 ) penalty function method is used to import the constrains into the objective function
( 4 )本文利用“惩罚函数法”将约束问题转变为无约束问题。 |
| 2. | Based on the variational characteristic of eigenvalue , the neural network solving method of other eigenvalues based on penalty function method was presented
从特征值的变分特性出发，给出了基于罚函数法的其他特征值的神经网络求解方案。 |
| 3. | Then the thesis give an example of impact - absorber . the example implement the multibody design optimization method together with dynamic analysis techniques and penalty function method
并以弹簧阻尼器为例，实现了了动力学分析与惩罚函数法相结合解决多体系统最优化问题的方法。 |
| 4. | The system build common multibody design optimization model , and for the first time using penalty function method and multibody dynamics implement the common multibody design optimization method
建立了多体系统优化的通用数学模型，首次采用惩罚函数方法实现了多体系统优化设计的通用方法。 |
| 5. | In fegm , the shape function is constructed by the moving least square ( mls ) approximation , the weak form of the equivalent integral equation to the governing equation is employed and essential boundary conditions are imposed by the penalty function method
它采用移动最小二乘法构造形函数，利用能量泛函的弱变分形式的积分方程，并用罚函数法施加本质边界条件，从而得到积分方程的数值解。 |
| 6. | So the improvement , such as the self - adaptive fitness function combined with the penalty function methods , self - adaptive crossover probability and the bp operator enlightened by neutral network , especially the bp network to improve the local optimal capacity were used
其次结合惩罚函数法对适应度进行了优化。采用自适应交叉概率。受神经网络中bp算法的启示，构造bp算子，提高小种群遗传算法的局部搜索寻优能力。 |
| 7. | In efgm , in order to get a numerical solution for a partial differential equation , the shape function is constructed by moving least squares ( mls ) , the control equation is derived from the weak form of variational equation and essential boundary conditions are imposed by penalty function method
它采用移动最小二乘法构造形函数，从能量泛函的弱变分形式中得到控制方程，并用罚函数法施加本质边界条件，从而得到偏微分方程的数值解。 |
| 8. | Such methods are generally decreasing method , such as , feasible direction methods , constrained variable metric methods , etc . another class is sub - problems method , which approximates the optimal solution by solving a series of simple sub - problems , such as penalty function methods , trust region methods , and successive quadratic programming sub - problems , etc . the same property of two classes of methods is that they determine whether the next iterative point is " good " or " bad " by comparing the objective function value or merit function value at the current point and next iterative point
另一类叫做子问题算法，这种算法是通过一系列简单子问题的解来逼近原问题的最优解，如罚函数法、信赖域算法、逐步二次规划算法等。这两类算法的一个共同特点是，通过比较当前点和下一个迭代点的目标函数值或评价函数值来确定迭代点的“优”或“劣” ，若迭代点比当前点“优”则该迭代点可以被接受，否则须继续搜索或调整子问题。 |
| 9. | The second chapter reveals the mathematical essence of entropy regularization method for the finite min - max problem , through exploring the relationship between entropy regularization method and exponential penalty function method . the third chapter extends maximum entropy method to a general inequality constrained optimization problem and establishes the lagrangian regularization approach . the fourth chapter presents a unified framework for constructing penalty functions by virtue of the lagrangian regularization approach , and illustrates it by some specific penalty and barrier function examples
第一章为绪论，简单描述了熵正则化方法与罚函数法的研究现状；第二章，针对有限极大极小问题，通过研究熵正则化方法与指数(乘子)罚函数方法之间的关系，揭示熵正则方法的数学本质；第三章将极大熵方法推广到一般不等式约束优化问题上，建立了拉格朗日正则化方法；第四章利用第三章建立的拉格朗日正则化方法，给出一种构造罚函数的统一框架，并通过具体的罚和障碍函数例子加以说明。 |
| 10. | On the basis of analyzing limitation of using penalty function method dealing with constraints , an amending method based on the knowlege regulating strategy is suggested to amende the mapping relationship of infeasible constraints in decoding , thus making the regulated individuals map into the space to obtain the most promising optimal solution
在分析惩罚函数法对约束处理效率较低的情况下，提出了一种基于知识调整策略的修正法，对解码中不满足约束的映射关系进行修正，使调整后个体映射到最有希望获得最优解的空间中。 |
