基于BP网络的反馈控制器设计

针对常规反馈控制器参数在对象时变情况下难以获得最优的问题，利用BP神经网络构成系统反馈控制器，通过自适应学习速率在线调整网络权值以逼近对象的逆动态模型，并利用Lyapunov方法给出了该算法的收敛的条件。将算法应用于循环水温度控制系统表明：该控制器对模型参数不依赖，能有效地适应控制对象参数的变化，系统具有较强的鲁棒性。     

焊接过程的RBF神经网络建模

介绍了RBF神经网络的性能和算法结构 ,建立了RBF神经网络在船舶焊接过程中用于焊接变形预测分析的模型 ,并探讨了其应用和发展趋势     

Hybrid SVM／HMM Method for Face Recognition

A face recognition system based on Support Vector Machine(SVM) and Hidden Markov Model (HMM) has been proposed. The powerful discriminative ability of SVM is combined with the temporal modeling ability of HMM. The output of SVM is moderated to be probability output, which replaces the Mixture of Gauss (MOG) in HMM. Wavelet transformation is used to extract observation vector, which reduces the data dimension and improves the robustness.The hybrid system is compared with pure HMM face recognition method based on ORL face database and Yale face database. Experiments results show that the hybrid method has better performance.     

基于学习的证券市场专家预测意见合成研究

针对证券市场的专家预测意见的特点，研究了将学习、证据理论与协同学理论引入证券市场专家预测意见合成的理论与方法．讨论了将专家的历史预测数据用基本可信数表示作为学习样本，对初选专家进行聚类分析选择有协同效应的专家．该方法用神经网络优化基本可信数的修正系数，再用Dempster合成规则进行预测意见的合成．实验结果表明该方法应用于解决实际问题时具有较好的效果．     

浅谈代数中的同余关系

同余关系在每一个代数分支的研究中都占据着重要的地位。本文以《泛代数》为指导 ,叙述半群、群、环、半环、模、半模等代数分支的同余关系 ,并讨论它们与子系统的关系 ,主要结果有命题 4、命题 7。     

广义正定矩阵的分析与应用

分析了广义正定矩阵,导出了矩阵广义正定的若干新判定准则,并以应用实例研究了其在神经网络中的应用.     

粉末冶金陶瓷模工艺中的快速脱蜡新技术

粉末冶金陶瓷模工艺是一种先进的过净形成形工艺．本文报道该工艺中一种新的脱蜡技术．采用快速加热、管径集中热量和定向导热传热综合方法，在陶瓷模和大部分蜡模材料处于较低温度下，使蜡模材料来不及发生显著的热膨胀而直接蒸发，得到脱蜡干净、外形完整的陶瓷模．     

HARICH-CHANDRA MODULES OVER LIE SUPERAL GEBRAS

In this paper, we obtain a theorem for the classification of the irreducible Harich-Chandra modules over G, Where G is a basic classical Lie superalgebra of I type. And the list of the classi-ficatication is given for G=A(1,0).     

古籍版本与校勘

以书的物质形态和表面特征来研究版本的学问就是版本学。古籍整理的重要任务就是选择好的版本，经过精心校勘，校正错讹，补充佚漏，删削衍文，适当标点和注释。只有版本好，校勘好，才能得到好的版本，达到整理古籍的目的。     

A ciosed-loop algorithm to detect human face using color and reinforcement learning

A closed-loop algorithm to detect human face using color information and reinforcement learning is presented in this paper. By using a skin-color selector, the regions with color “like“ that of human skin are selected as candidates for human face. In the next stage, the candidates are matched with a face model and given an evaluation of the match degree by the matching module. And if the evaluation of the match result is too low, a reinforcement learning stage will start to search the best parameters of the skin-color selector. It has been tested using many photos of various ethnic groups under various lighting conditions, such as different light source, high light and shadow. And the experiment result proved that this algorithm is robust to the vary-ing lighting conditions and personal conditions.