| 1. | Robust design method of predictive controller parameter based on min - max ruler
的预测控制鲁棒参数设计 |
| 2. | An approximation algorithm for min - max tree partitioning and an optimal algorithm for min - sum tree partitioning
最小最大树划分的近似算法与最小和树划分的精确算法 |
| 3. | ( 3 ) dual - rate min - max robust generalized predictive control algorithm is proposed , which adds a constraint output horizon to improve the system properties
( 3 )给出了一类新的双速率采样min - max鲁棒广义预测控制 |
| 4. | Objectives evaluation function is made by using virtual objective method and min - max method . genetic algorithms are used to solve the optimization problem quickly and sufficiently
综合虚拟目标法和极小极大法，构造两个优化目标的评价函数，然后采用遗传算法寻优。 |
| 5. | A new merit function ( potential function ) is constructed in chapter three , based on the balanced effect of the min - max problem . because of the non - smoothness , shannon entropy is used to smooth the merit function
鉴于该效益函数是非光滑的，不便于操作，使用前一章给出光滑函数做了光滑处理，而得到的光滑效益函数是一个凸函数。 |
| 6. | In this thesis , we extend the entropy regularization method in two ways : from the min - max problem to general inequality constrained optimization problems and from the entropy function to more general functions
本文从两个方面发展了这种熵正则化方法，即将其从极大极小问题推广到一般不等式约束优化问题上和用一般函数代替熵函数作正则项，建立新的正则化方法。 |
| 7. | To effectively extract edges , edge pixels are classified according to the difference of intensity distribution , and multiple effective edge features are defined . edges are detected by applying fuzzy reasoning ( min - max centroid method ) , based on local image edge characteristics
该方法在重新定义边缘特征的基础上，引入模糊推理理论，从而形成了基于多边缘特征和模糊推理的边缘检测方法。 |
| 8. | Maximum entropy method is an effective smoothing one for the finite min - max problem , which , by adding shannon ' s informational entropy as a regularizing term to the lagrangian function of min - max problem , yields a smooth function that uniformly approaches the non - smooth max - valued function
极大熵方法是解有限极大极小问题的一种有效光滑化法，它通过在极大极小问题的拉格朗日函数上引进shannon信息熵作正则项，给出一致逼近极大值函数的光滑函数。 |
| 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. | According to specialists ? experience , such dangerous factors as water temperature , water pressure , oxygen density in the tank , the temperature and pressure of water vapor , ect . as well as control rules are made out in safety regulations for oil tankers . by using fuzzy logic ( min - max reasoning ) , all sorts of fuzzy information are processed so as to simplify the control procedure in addition to realize it
其方法是利用模糊逻辑控制的优点，不需建立精确的数学模型，根据专家的经验，油轮安全规则制定出各危险因素（如水温，水压，氧气浓度，压力，温度等）的模糊隶属函数和一系列的控制规则，利用模糊逻辑（ min max ）来处理各种模糊信息，使整个推理过程运算简单易于实现。 |
