大学物理演示实验网络教学管理平台构建与实施

详细介绍了我校开发的“大学物理演示实验网络教学管理平台”的框架,其在教学管理中所发挥的作用,运行的效果,特别是在低年级本科生创新能力培养中所显现的效果.     

基于高频雷达多普勒谱预测风向的一种新方法

通过高频雷达一阶多普勒谱与海上风向的关系, 提出了一种基于神经网络和多波束采样法相结合预测海面风向的新方法. 在神经网络不同输入、输出参数情况下对扩展因子为奇数时的仿真数据进行了风向预测, 并在扩展因子为偶数时结合多波束采样法进行风向预测, 消除了风向的模糊性. 通过预测数据和仿真数据对比, 发现两者符合较好. 从神经网络和多波束采样法相结合的预测结果中可以看出, 风向的误差大约为4°—6°, 风向扩展因子的平均误差为0.26, 为预测海面风场的研究提供一种新的思路和方法.     

液体空分流程形式浅析

通过对全液体空分装置不同流程组织形式进行分析和模拟计算、能耗与投资的比较,根据不同规格的产品要求,进行合适的流程形式选择,以可达到节能降耗的目的。     

Effects of Some Topological Ingredients on the Evolutionary Ultimatum Game

This study aims at figuring out the crucial topological ingredients which affect the outcomes of the ultimatum game located on different networks,encompassing the regular network,the random network,the small world network,and the scale-free network.With the aid of random interchanging algorithm,we investigate the relations between the outcomes of the ultimatum game and some topological ingredients,including the average range,the clustering coefficient and the heterogeneity,and so forth.It is found that for the regular,random and small-work networks,the average range and the clustering coefficient have evident impacts on the ultimatum game,while for the scale-free network the original degree heterogeneity and the underlying rich-club characterizations are the mainly important topologica ingredients that influence the outcomes of ultimatum game substantially.     

基于多种群混沌遗传神经网络的模拟电路故障诊断

针对标准遗传算法优化BP神经网络收敛慢,易陷入局部最优的问题,提出了改进的多种群协同进化遗传算法,该算法改变了以往的随机初始化方法,采用了附加混沌扰动的tent映射初始化均匀分布的种群,提高了初始解的质量;每个种群采用自适应交叉率和变异率,引入移民算子实现种群间的横向联系;算法通过多种群的协同进化和种群间的个体移植提高了算法的搜索均匀性和效率;仿真实验表明该算法误差小,收敛速度快,诊断正确率高,较好地解决了模拟电路的软故障诊断问题。     

结构与参量不确定的网络与网络之间的混沌同步

进行了结构与参量不确定的网络与网络之间的混沌同步研究．通过设计适当的控制输入,不但实现了两个复杂网络之间的混沌同步,而且网络节点状态方程中的未知参量和网络内部节点之间的耦合强度也被同时确定．通过采用具有调制损耗的CO2激光器的状态方程进行仿真实验,发现网络与网络之间的同步性能非常稳定．     

应用可见/近红外光谱技术快速检测果珍中二氧化钛的含量

为了实现快速检测果珍中的二氧化钛含量,提出了应用近红外光谱技术结合化学计量学的快速检测方法。研究采用了320份果珍样本进行光谱特性的检测,其中200个样本用来建模,120个样本进行预测。首先比较了标准正态变量校正(SNV)、变量标准化(Normalize)、多元散射校正(MSC)等6种不同的数据预处理方法对偏最小二乘法(PLS)建模预测效果的影响。然后将PLS模型与应用主成分(PC)建立的主成分-神经网络校正(PC-ANN)模型进行比较。结果表明,MSC预处理的效果最好,PLS模型的最佳主成分数为7,预测值与标准值的相关系数R2达0.900 8,预测标准误差RMSEP为0.05。PC-ANN模型预测值与标准值的R2为0.868 4,RMSEP为0.04。说明PLS模型比PC-ANN模型的预测效果好。同时本研究也说明能够应用可见/近红外技术对二氧化钛进行快速定量测定。     

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation.It treats the DFT as a black box and models the error through external statistical information.As a demonstration,the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides.Through the AGANN correction,the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set.For comparison,the neural network approach reduces the mean value to 2.56%.And for the common combinational approach of genetic algorithm and neural network,the value drops to 2.15%.The multiple linear regression method almost has no correction effect here.     

Adaptive co-evolution of strategies and network leading to optimal cooperation level in spatial prisoner’s dilemma game

We study evolutionary prisoner's dilemma game on adaptive networks where a population of players co-evolves with their interaction networks. During the co-evolution process, interacted players with opposite strategies either rewire the link between them with probability $p$ or update their strategies with probability $1-p$ depending on their payoffs. Numerical simulation shows that the final network is either split into some disconnected communities whose players share the same strategy within each community or forms a single connected network in which all nodes are in the same strategy. Interestingly, the density of cooperators in the final state can be maximised in an intermediate range of $p$ via the competition between time scale of the network dynamics and that of the node dynamics. Finally, the mean-field analysis helps to understand the results of numerical simulation. Our results may provide some insight into understanding the emergence of cooperation in the real situation where the individuals' behaviour and their relationship adaptively co-evolve.     

Global Synchronization of Directed Networks with Fast Switching Topologies

Global synchronization of a class of directed dynamical networks with switching topologies is investigated. It is found that if there is a directed spanning tree in the fixed time-average of network topology and the time-average is achieved sufficiently fast, then the network will reach global synchronization for sufficiently large coupling strength.