动态校正的PI型广义预测控制器及其在反应釜系统中的仿真研究

提出了一种新型的广义预测控制器。由于在控制器的设计中,进行了预测误差的动态校正,并且控制器采用了比例积分型(PI)目标函数,因此该控制器对未建模动态具有很强的鲁棒性。应用低阶的模型对一个强放热的化学反应釜模型进行的仿真研究检验了该方法的有效性。     

关于Farkas引理证明的一点注记

许多关于非线性规划的专著,如[1]、[2]、[3]等,在证明Farkas引理时都蕴含地用到:“有限生成的凸锥(finitely generated convex cone)为闭集”这样一个事实,但都忽略了对此作出证明。本文旨在于非线性规划的内容范围内证明这一重要事实,从而对上述专著中Farkas引理的证明作出补充。     

Banach空间中一类强非线性发展方程的反周期解

本文讨论一类强非线性发展方程的反周期解的存在性。针对一大类既含有单调非线性算子又含有非单调非线性算子的发展方程。我们巧妙地结合单调算子理论与Leray-Schauder不动点理论，证明了其反周期解的存在性。最后，举例说明理论结果在2m阶拟线性抛物型方向的时间反周期问题中的应用。     

不连续二阶周期边值问题的可解性

考察了一类非线性项含有一阶导数的二阶周期边值问题的解的存在性,其中非线性项是Carathèodory函数.通过构造非线性项的高度函数并且利用Leray-Schauder不动点定理建立了两个存在定理.第一个定理表明只要高度函数的积分是适当的,这类问题至少有一个解.第二个定理表明当非线性项在无穷远处增长的极限是一个无界函数时在适当条件下这问题仍可能有一个解.     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

The controlling factors and distribution prediction of H2S formation in marine carbonate gas reservoir, China

Generally, there are some anhydrites in carbonate reservoir, as H2S is also familiar in carbonate oil and gas reservoirs. Nowadays, natural gas with high H2S concentration is usually considered as TSR origin, so there is close relationship between H2S and anhydrite. On the contrary, some carbonate rocks with anhydrite do not contain H2S. Recently, researches show that H2S is only a necessary condition of H2S formation. The reservoir porosity, sulfate ion content within formation water, reservoir temperature, oil/gas and water interface, hydrocarbon and some elements of reservoir rock have great controlling effects on the TSR occurrence. TSR deoxidizes hydrocarbon into the acidic gas such as H2S and CO2, and the H2S formation is controlled by TSR occurrence, so the relationship among reaction room, the contact chance of sulfate ion and hydrocarbon, the reservoir temperature has great influence on the TSR reaction. H2S has relatively active chemical quality, so it is still controlled by the content of heavy metal ion. Good conditions of TSR reaction and H2S preservation are the prerequisite of H2S distribu- tion prediction. This paper builds a predictive model based on the characteristic of natural gas reservoir with high H2S-bearing. In the porosity reservoir with anhydrite, the formation water is rich in sulfate and poor in heavy metal ion. Oil and gas fill and accumulate in the gas reservoir with good preservation conditions, and they suffered high temperature later, which indicates the profitable area of natural gas with high H2S-bearing.     

第二类非线性Fredholm型积分方程数值解的超收敛性

本文讨论第二类非线性Ｆｒｅｄｈｏｌｍ型积分方程数值解的超收敛性，以Ｇａｌｅｒｋｉｎ方法为基础建立了该类方程的Ｇａｌｅｒｋｉｎ算法、小波Ｇａｌｅｒｋｉｎ算法以及它们相应的迭代校正格式，证明了两种算法数值解的超收敛性，不仅将Ｈａｍｍｅｒｓｔｅｉｎ积分方程的结果推广到第二类非线性Ｆｒｅｄｈｏｌｍ型积分方程，而且应用小波分析工具得到了更精确的结果．     

螺旋霉素发酵过程动态建模的神经网络方法

对文题所述方法,根据实验数据进行仿真研究,获得了满意的结果。为高度复杂的非线性生化过程的模型化提供了一条新的途径。     

非线性系统两变量控制下的分枝特性

采用分枝理论推导非线性系统两变量P和PI控制下的分枝特性,所导得的几个定理阐明了简单分枝点和Hopf分枝点的位置和性质。一个简化精馏塔模型用于演示这一过程,展示出精馏塔系统在闭环时,可能出现多态和极限环。