optimal disjunctive normal form

optimal disjunctive normal form的基本解释

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最优析取范式

与optimal disjunctive normal form相关的网络例句 [注：此内容来源于网络，仅供参考]

Through a discussion on the suffix features of miniterm and maxiterm ,this paper offers a suffix calculating approach to miniterm and maxiterm in principal disjunctive normal form,principal conjunctive normal form determined by disjunctive normal form,and conjunctive normal form of propositional formula.

通过极小项和极大项的下标特征的讨论，给出了由命题公式的析取范式、合取范式而确定的主析取范式、主合取范式中的极小项、极大项的下标计算方法，从而简化了由繁杂的命题公式推演或真值计算求主范式的计算过程。

On this basis, according to historical data, apply ANN and differential simulation method to get the quantitatively correlative relations between each production and its own influence factors, and introduce the new methods of prediction for dynamic indexes with gas-field development (The combinatorial prediction method based on fuzzy comprehensive evaluation, the method of ANN to select optimally combinatorial prediction models and the ANN prediction method based on genetic algorithm).(2) Base on mathematical programming, combine with quantitative economics and techno-economics, introduce economical indexes to establish production"s distribution optimal model, production"s constitution optimal model and measured production"s constitution optimal model, including multi-objective models and five-years models. Upon this, the optimal project for all gas field and each gas-collected factory can be got. Also, introduce the time value of capitals to improve on these models.(3) Base on the optimal solution theory and algorithm theory for the nonlinear programming problem, introduce the SUMT algorithm and genetic algorithm to study how to solve the models, and on the basis of normal genetic algorithm, make use of auto-adaptively modulating method to improve on normal genetic algorithm; Base on algorithm"s convergence theory and calculation"s complexity theory to analyze seriatim SUMT algorithm"s convergence and genetic algorithms convergence, and compare performance with each other.

在此基础上，利用神经网络方法和微分模拟方法根据历史数据得到各分项产量与其影响因素之间的定量关联关系，并引入气田开发动态指标新的预测方法(基于模糊综合评判的组合预测方法、神经网络优选组合预测模型预测方法以及基于遗传优化的神经网络预测方法)；(2)以数学规划为基础，结合数量经济学和技术经济学，引入经济指标建立产量分配优化模型、产量构成优化模型、措施产量构成优化模型、气田开发多目标规划模型以及五年规划模型，进而获得全气田及各采气厂的最优方案，并引入资金时间价值对五年规划模型进行改进；(3)以非线性规划问题的最优解及算法理论为基础，引入SUMT算法以及遗传算法对模型的求解进行研究，并在原有的遗传算法基础上，引入自适应调整方法对遗传算法进行改进；以算法的收敛性理论和计算复杂性理论为基础，逐一分析SUMT算法以及遗传算法的收敛性，并比较三种算法的优劣性。

Results l.Serum concentrations of VCAM-1 in PIH (93.39 + 57.3)ug/ml were higher than that in normal pregnant group. Serum concentrations of VCAM-1 in moderate(97.89 + 34.07)ug/ml and severe PIH(132.24 + 60.97)ug/ml were significantly higher than that in normal group.(P.05, P.01). There was no difference between serum levels of VCAM-1 mild PIH and normal pregnant group. 2.Serum levels of IL-6 in PIH group(102.17 ?48. 31)pg/ml were significantly higher than that in normal group(49.16 + 12.9)pg/ml.Serum levels of IL-6 in moderate(95.79+31.19)pg/ml and severe PIH( 127.27+11.3 8)pg,ml were significantly higher than that in normal group. There was no difference between serum levels of PIH in mild PIH (52.13 + 12.90)pg/ml and normal pregnant group. 3. In PIH group, serum concentrations of VCAM-1 correlate with the levels of IL-6,r=0.63. 4.The expression of VCAM-1 in cytotrophoblast of spiral arteries in normal pregnant group(100%) were significantly higher than that in PIH group.Theexpression of VCAM-1 in moderate PIH(37.50%) and sever PIH were lower than that in normal group.There was no difference between the mild PIH and normal group.Conclusions The increased levels of serum of VCAM-1 may participate in the process of vascular endothelium damages in PIH.

结果 1、妊高征组血清VCAM-1浓度为(93.39±57.3)μg／ml明显高于正常妊娠组(44.87±15.60)μg／ml，差别有显著性(P＜0.05)；中、重度妊高征组VCAM-1浓度分别为(97.89±34.07)μg／ml和(132.24±60.97)μg／ml，与正常妊娠组比较，差异有显著性(p＜0.05)和非常显著性(P＜0.01)；轻度妊高征组VCAH-1为(48.46±15.60)μg／ml与正常妊娠组比较，差异无显著性(P＞0.05)。2、妊高征组血清IL-6含量为(102.17±48.31)pg／ml，明显高于正常妊娠组(49.16+12.9)pg／ml，差异有非常显著性(P＜0.01)；中、重度妊高征组IL-6含量分别(95.79±31.19)pg／ml和(127.27±11.38)pg／ml，与正常妊娠组比较，差异有显著性(P＜0.05)和非常显著性(P＜0.01)；轻度妊高征组IL-6含量为(52.13±12.90)pg／ml与正常妊娠组比较，差异无显著性(P＞0.05)。3、VCAM-1与IL-6水平呈明显正相关，r=0.63(P＜0.01)。4、子宫胎盘床螺旋动脉滋养细胞VCAM-1表达，正常妊娠组都存在阳性表达(阳性表达率100％)，妊高征组有10例阳性表达阳性表达率为叩们，差别有非常显著性河＜0.01L 中、重度妊高征组阳性表达率分别为37、50％和0，与正常妊娠组比较，差别有显著性河＜0.05）和非常显著性汀＜0.01太轻度妊高征组阳性表达率为83.33凡与正常妊娠组差别无显著性问＞0.05）。

与optimal disjunctive normal form相关的网络解释 [注：此内容来源于网络，仅供参考]

optimal disjunctive normal form：最优析取范式

optimal control 最优控制 | optimal disjunctive normal form 最优析取范式 | optimal normal form 最优标准形