基于麻雀搜索算法结合深度前馈神经网络的近红外模型转移方法研究

该文提出了一种基于麻雀搜索算法结合深度前馈神经网络（SSA-DFN）的近红外光谱模型转移方法。使用深度前馈神经网络拟合不同仪器采集到的光谱之间的非线性函数映射,并将麻雀搜索算法用于网络各层连接权值和阈值的初始化,通过种群中个体位置的迭代更新,求得连接权值和阈值的最优初始值;通过多次调整深度前馈神经网络模型的超参数,使网络拟合效果趋于最优,最终确定转移函数。为验证方法的有效性,分别从烟叶近红外光谱谱图、主成分投影和预测结果的角度,将SSA-DFN方法与分段直接校正算法（PDS）、典型相关性分析算法（CCA）转移前后的效果进行了对比。结果表明SSA-DFN方法转移后的从机光谱与原主机光谱重合度最高,转移后主、从机总糖、烟碱含量的预测结果差异不显著,预测平均误差从8.32%、9.15%分别降至4.65%、4.82%,预测均方根误差（RMSEP）和决定系数（R2）等指标均优于PDS和CCA,取得了最佳的转移效果,可满足企业需求。结果表明该方法是一种有效的模型转移方法。     

浮筏的主动吸振控制实验研究

浮筏在低频减振方面存在较大不足,而这恰是主动减振的优势.将两者相结合,研究了浮筏的主动吸振控制问题.研制了一种主动吸振器,建立了以DSP F2812为核心的控制系统;提出了符号LMS算法,与广泛使用的滤波x-LMS等算法相比,该算法不需要精确的模型信息;在此基础上设计了自适应前馈主动吸振控制律,并在浮筏实验平台上进行主动吸振控制实验.实验结果表明,通过主动吸振控制,浮筏的低频减振能力得到了显著的改善.     

时域自适应精细算法求解对流热传导问题

应用时域自适应精细算法求解对流传热问题.通过展开技术,可更准确地描述变量随时间的变化,同时将时空耦合的初边值问题转化为一系列的空间边值问题,并采用有限元方法递推求解.自适应技术可弥补时间步长不同时可能造成的计算精度损失.并进行了数值检验.     

SELF-ADAPTIVE STRATEGY FOR ONE-DIMENSIONAL FINITE ELEMENT METHOD BASED ON ELEMENT ENERGY PROJECTION METHOD

Based on the newly-developed element energy projection (EEP) method for computation of super-convergent results in one-dimensional finite element method (FEM), the task of self-adaptive FEM analysis was converted into the task of adaptive piecewise polynomial interpolation. As a result, a satisfactory FEM mesh can be obtained, and further FEM analysis on this mesh would immediately produce an FEM solution which usually satisfies the user specified error tolerance. Even though the error tolerance was not completely satisfied, one or two steps of further local refinements would be sufficient. This strategy was found to be very simple, rapid, cheap and efficient. Taking the elliptical ordinary differential equation of second order as the model problem, the fundamental idea, implementation strategy and detailed algorithm are described. Representative numerical examples are given to show the effectiveness and reliability of the proposed approach.     

Novel high-voltage self-adaptive power device based on interface charge＊

This paper presents a novel high-voltage lateral double diffused metal-oxide semiconductor （LDMOS） with self- adaptive interface charge （SAC） layer and its physical model of the vertical interface electric field. The SAC can be self-adaptive to collect high concentration dynamic inversion holes, which effectively enhance the electric field of dielectric buried layer （EI） and increase breakdown voltage （BV）. The BV and EI of SAC LDMOS increase to 612 V and 600 V/tim from 204 V and 90.7 V/ttm of the conventional silicon-on-insulator, respectively. Moreover, enhancement factors of r/which present the enhanced ability of interface charge on EI are defined and analysed.     

Decomposition Method with a Variable Parameter for a Class of Monotone Variational Inequality Problems

In this paper, we focus on a useful modification of the decomposition method by He et al. (Ref. 1). Experience on applications has shown that the number of iterations of the original method depends significantly on the penalty parameter. The main contribution of our method is that we allow the penalty parameter to vary automatically according to some self-adaptive rules. As our numerical simulations indicate, the modified method is more flexible and efficient in practice. A detailed convergence analysis of our method is also included.     

A self-adaptive trust region method for the extended linear complementarity problems

By using some NCP functions, we reformulate the extended linear complementarity problem as a nonsmooth equation. Then we propose a self-adaptive trust region algorithm for solving this nonsmooth equation. The novelty of this method is that the trust region radius is controlled by the objective function value which can be adjusted automatically according to the algorithm. The global convergence is obtained under mild conditions and the local superlinear convergence rate is also established under strict complementarity conditions. This work is supported by National Natural Science Foundation of China (No. 10671126) and Shanghai Leading Academic Discipline Project (S30501).     

高精密运动工作台控制系统的设计与研究

设计并研制了用于母盘刻录机的高精密运动工作台及其控制系统。工作台控制系统采用线光栅作为工作台位移检测工具。采用数字位置环和模拟电流环组成双闭环随动系统,其中位置控制器是带有速度前馈和加速度前馈的数字PID伺服滤波器。实验结果表明,该系统可以满足母盘刻录机的要求。     

基于自适应BP神经网络的结构损伤检测

描述基于人工神经网络的结构损伤检测的基本步骤以及该方法在实际5层钢框架结构损伤检测上的应用．提出了一种改进的BP神经网络方法，它能够解决传统BP神经网络在实际应用中存在的两个问题：收敛速度慢并存在局部极小．其基本思想是引入动态自适应算子加速传统BP算法的梯度下降速度，从而提高运算速度，通过自调节保证学习过程中每一时刻具有较大的sigmoid函数值，从而可以避免局部极小．数值仿真结果表明基于该自适应神经网络的结构损伤检测方法具有强的鲁棒性，而且与传统的BP神经网络相比，不仅提高了计算速度，并且具有很高的精度．最后，实例的应用也证明了该方法的有效性．     

自适应卡尔曼滤波在惯性测量组合误差补偿中的应用

惯性元件误差是捷联惯导系统的主要误差源，必须在导航过程中加以补偿。根据机动目标跟踪理论和惯性测量组合动态模型，分别建立状态方程和观测方程，利用机动频率自适应的算法进行卡尔曼滤波，以此达到惯性测量组合动态误差和随机误差补偿的目的。仿真结果说明该方法可行有效，优于传统的误差补偿算法，能较好地提高系统导航精度。