空间直线问题的几种解法

对空间解析几何中一道求直线方程题目的解法进行多角度探讨,给出4种解法,以便寻求出最佳解法,从而引导学生形成多方位的解题思路,达到扩展学生思维的目的.     

局部凸拓扑空间中凝聚映象的新不动点定理

讨论了局部凸拓扑向量空间中凝聚映象的不动点,从而获得了一些新的不动点定理。     

关于向量组线性相关性的初步探讨

向量组的线性相关性是线性代数中的一个重点和难点,介绍了向量组线性相关性的概念与几何意义,提出用几何的理念加深对其概念的理解,通过例子证明线性相关性来从总体上对其判定进行梳理和把握。     

Banach空间中一类广义向量变分不等式解的存在性

在Banach空间中引入和研究了一类具有更一般形式的广义集值向量变分不等式问题,证明了此类变分不等式解的存在性,并得到了一些解的性质.     

解析几何课堂教学的思考

本文结合笔者在教学过程中的体会,深入探讨了解析几何课程的课程特点及教学方法。     

智能电网视角下粒子群优化支持向量机的用电量预测

智能电网的—个重要特征是通过高精度的用电量预测进行电能智能调配,用电量信息的精确预测是电网智能化的关键指标.针对用电量数据非线性的特点,提出了一种基于粒子群优化的PSO-CV-SVR模型.该模型基于支持向量回归机原理,以粒子群算法和交叉验证的思想优化模型参数.将该模型应用于江苏省全社会用电量的预测分析,结果表明该模型优于BP-神经网络方法,提高了预测的精度.     

基于支持向量机的企业突破性创新识别模型研究

根据文献研究和创新识别的内涵,从技术、市场、产品、管理、财务、环境6个维度构成创新模式识别因素指标集,利用支持向量机原理建立了企业突破性创新识别模型,并应用该模型对61个样本数据进行创新模式的仿真识别,结果发现输出结果与样本调研及实际创新模式是一致的。实证研究表明,该方法在突破性创新识别中具有较强的有效性及优越性。     

Social relation extraction from texts using a support-vector-machine-based dependency trigram kernel

We propose a social relation extraction system using dependency-kernel-based support vector machines (SVMs). The proposed system classifies input sentences containing two people’s names on the basis of whether they do or do not describe social relations between two people. The system then extracts relation names (i.e., social-related keywords) from sentences describing social relations. We propose new tree kernels called dependency trigram kernels for effectively implementing these processes using SVMs. Experiments showed that the proposed kernels delivered better performance than the existing dependency kernel. On the basis of the experimental evidence, we suggest that the proposed system can be used as a useful tool for automatically constructing social networks from unstructured texts.     

基于LS-SVM的大理州入境游客流量组合预测模型

大理州入境旅游客流量的持续增长引起了大理州政府和旅游业界的高度关注。而对未来入境旅游客流量及时准确的预测成为制定旅游规划的重要依据。依据大理州历年游客流量的数据,提出了基于线性回归、算术移动平均、指数平滑和灰色系统等方法的最小二乘支持向量机大理州入境旅客流量组合预测模型。通过实验仿真,表明该模型具有较高的预测精度,为大理州入境游客流量预测提供了新的方法。     

Application of biomonitoring and support vector machine in water quality assessment

The behavior of schools of zebrafish (Danio rerio) was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine (SVM) was developed. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration (LC₅₀) of a pollutant. The data were used to develop a method to evaluate water quality, so as to give an early indication of toxicity. Four kinds of metal ions (Cu²⁺, Hg²⁺, Cr⁶⁺, and Cd²⁺) were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm (GA) was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.