线性代数中特征向量的一个应用

在线性代数中,特征向量在矩阵的对角化过程中起着重要作用.从一个引例出发,证明了:一个矩阵与对角矩阵可交换当且仅当它可以用以特征向量为列向量的两个矩阵表示.做为推论,如果对角矩阵对角线上的相同元素在相邻位置,那么与其可交换的矩阵只能是准对角矩阵.     

混沌时间序列的模糊神经网络预测

设计一种新型混合模糊神经推理系统，该系统仅从期望输入输出数据集即可达到获取知识、确定模糊初始规则基的目的.再利用神经网络学习能力便不难修改规则库中的模糊规则以及隶属函数和网络权值等参数，这样大大减少了规则匹配过程，加快了推理速度，从而极大程度地提高了系统的自适应能力.用它对Mackey-Glass混沌时间序列进行预测试验,结果表明利用该网络模型无论离线还是在线学习均能对Mackey-Glass混沌时间序列进行准确的预测，证明了该系统的有效性. 关键词： 神经网络模型 模糊逻辑 混合推理系统 混沌时间序列     

基于VHSE结构表征的TAP亲和活性预测及选择特异性分析

应用氨基酸描述子VHSE(Principal component score vector of hydrophobic, steric, and electronic properties)对613个抗原9肽进行结构表征, 在此基础上, 采用支持向量机结合逐步回归变量筛选方法, 成功建立了抗原肽抗原处理相关转运蛋白(Transporter associated with antigen processing, TAP)亲和活性预测模型, 最优线性支持向量机模型的R2, Q2和R2ext分别为0.7386, 0.7270和0.6057. 模型结果分析表明, 影响TAP亲和活性的首要因素是电性, 其次是立体和疏水性质; 底物9肽的P1(N端)及P2, P7和P9(C端)位氨基酸物化性质对TAP亲和活性有重要影响, 而P3, P4, P5和P6位对模型贡献相对较小, P8位则与活性无关. 依据最优模型对模拟点突变9肽的TAP亲和活性的预测结果, 并结合变量载荷分析, 对TAP底物选择特异性进行了分析和总结.     

LMI-based output feedback fuzzy control of chaotic system with uncertainties

This paper studies the robust fuzzy control for nonlinear chaotic system in the presence of parametric uncertainties. An uncertain Takagi-Sugeno （T-S） fuzzy model is employed for fuzzy modelling of an unknown chaotic system. A sufficient condition formulated in terms of linear matrix inequality （LMI） for the existence of fuzzy controller is obtained. Then the output feedback fuzzy-model-based regulator derived from the LMI solutions can guarantee the stability of the closed-loop overall fuzzy system. The T-S fuzzy model of the chaotic Chen system is developed as an example for illustration. The effectiveness of the proposed controller design methodology is finally demonstrated through computer simulations on the uncertain Chen chaotic system.     

Adaptive regulation of uncertain chaos with dynamic neural networks

A novel adaptive control for uncertain nonlinear chaotic system is presented. A dynamical neural networks is used to perform ‘black box' identification. Based on the identifier, the state feedback control is employed to drive the unknown chaotic system toward the desired target. Simulations show the derived control via the neuro-identifier turns out to be very effective.     

混沌系统的遗传神经网络控制

提出遗传神经网络控制混沌新方法.将小扰动技术和周期控制技术结合起来，用遗传算法训练神经网络，使之成为混沌控制器.对Henon映射和Logistic映射的仿真结果说明控制器能产生小扰动控制序列信号，将混沌振荡转变成规则运动状态.该方法无需了解动态系统数学模型，具有一定抗噪声干扰能力，可将它推广应用到其他混沌系统的控制中. 关键词： 遗传算法 神经网络 混沌 周期控制     

非线性系统混沌运动的神经网络控制

设计前馈反传神经网络控制非线性系统混沌运动的新方法.根据扰动参数模型输入输出数据,按照非线性学习算法训练网络产生系统稳定所需的小扰动控制信号,去镇定混沌运动,使嵌入在混沌吸引子中的不稳定周期轨道回到稳定不动点上.Hnon映射数值仿真结果表明,这种方法控制非线性混沌系统响应速度快、控制精度高 关键词： 混沌控制 神经网络 吸引子 非线性     

Advances in Structure-function Relationships and Mechanism of Transporter Associated with Antigen Processing

The transporter associated with antigen processing (TAP) belongs to the ATP-binding cassette (ABC) transporter superfamily. Driven by ATP hydrolysis, TAP translocates antigenic peptides from the cytosol into the ER (endoplasmic reticulum) lumen where the antigenic peptides are loaded onto the HLA class Ⅰ molecules. Recently, numerous studies show that TAP is closely related with various diseases such as viral infections, autoimmune diseases, and different malignancies. In consideration of important roles of TAP in human adaptive immunity, this review summarizes the recent advances in structure-function relationships of crucial domains and transport mechanism systematically. The challenging problems and potential methods are also pointed out for in-depth researches.     

Robust fuzzy control for chaotic dynamics in Lorenz systems with uncertainties

This paper deals with the robust fuzzy control for chaotic systems in the presence of parametric uncertainties. An uncertain Takagi--Sugeno fuzzy model for a Lorenz chaotic system is first constructed. Then a robust fuzzy state feedback control scheme ensures the control for stable operations under bounded parametric uncertainties. For a chaotic system with known uncertainty bounds, a robust fuzzy regulator is designed by choosing the control parameters satisfying the linear matrix inequality. To verify the validity and effectiveness of the proposed controller design method, an analysis technique is suggested and applied to the control of an uncertain Lorenz chaotic system.     

不确定混沌系统的回归神经网络同步

将高阶连接的神经元融合到分布式回归神经网络，研究了出现非模型动态性时不确定混沌系统的辨识和同步问题。采用李雅谱诺夫稳定理论对高阶神经网络回归模型的权值进行学习更新，同时，获取整个系统稳定特性的分析结果，而且通过李雅谱诺夫方法设计出消除不确定混沌系统的同步误差的自适应控制律。最后将所提出的方法应用到不确定Rossler混沌系统的建模与同步