基因芯片点样机驱动系统的设计与实现

基因芯片点样机是完成基因芯片点样工作的一种自动化系统。该系统的运动驱动程序是保证点样精度的关键部分。面向对象技术在很多方面得到了成功的应用。将面向对象技术应用于基因芯片点样系统的驱动控制，设计并实现了基因芯片点样机的驱动程序。该软件被成功地应用于基因芯片的制备，具有控制精度高和操作方便等优点。     

一种新型维纳系统及其在混沌系统建模中的应用

提出了一种新型的维纳系统，能以任意精度逼近动态非线性对象。证明了此系统在一定条件下，逼近动态非线性系统离散点列的精度优于传统的以神经网络为静态非线性环节的维纳系统。此系统还具有运算量小的特点，易于实现对动态非线性系统进行在线建模与控制。     

一种基于多分辨分析的小波压噪方法

本文基于小波变换的多分辨分析算法,提出了一种对受白噪声污染的数据进行除噪处理的算法,尤其适用于对含有高频分量的信号进行除噪.仿真实验表明该算法既能有效除噪又能保护真实信息的细节特征.     

基于遗传算法的有约束非线性预测控制

将神经网络辨识、遗传算法全局优化和预测控制思想有机结合,提出了一种新型控制器,用于带约束的非线性对象的控制.仿真结果表明此算法在非线性控制方面的优异性能.     

基于神经网络的车牌照自动识别系统

本文就车牌照倾斜度校正、车牌定位、字符切分、字符识别及处理进行了详细的讨论.本文采用BP网络对车牌照字符进行学习分类,取得较好效果.     

基于IMC结构的PID-GPC的鲁棒性分析

Proportion integral differential generalized predictive control(PID-GPC),a new type of generalized predictive control(GPC) is introduced,and its quality is analyzed with internal model contron(IMC).A very important characteristic,which distinguishes GPC from ordinary IMC,and the robust effect are found.At the same time,a robust region is obtained according to the control laws,so that the defect that the robust analysis could be carried out only with stable models is vercome.It is verified that the robustness of PID-GPC is stronger than general GPC.     

基因芯片微点阵仪的环境预测控制

在基因芯片的制作过程中,对周围环境的温、湿度要求较高,周围环境的好坏将直接影响着基因芯片的质量.本文介绍了基因芯片微点阵仪环境控制系统的设计及实现,该系统能实现对温湿度的高精度调节.     

双向联想记忆神经网络抗噪声的能力

This paper analyzes noise sensitivity of bidirectional association memory (BAM) and shows that the anti-noise capability of BAM relates not only to the minimum absolute value of net inputs(MAV), as some researchers found, but also to the variance of weights associated with synapse connections. In fact, it is determined by the quotient of these two factors. On this base, a novel learning algorithm—small variance leaning for BAM(SVBAM) is proposed, which is to decrease the variance of the weights of synapse matrix. Simulation experiments show that the algorithm can decrease the variance of weights efficiently, therefore, noise immunity of BAM is improved. At the same time, perfect recall of all training pattern pairs still can be guaranteed by the algorithm.