Negative effects of sufficiently small initial weights on back-propagation neural networks

In the training of feedforward neural networks,it is usually suggested that the initial weights should be small in magnitude in order to prevent premature saturation.The aim of this paper is to point out the other side of the story:In some cases,the gradient of the error functions is zero not only for infinitely large weights but also for zero weights.Slow convergence in the beginning of the training procedure is often the result of sufficiently small initial weights.Therefore,we suggest that,in these cases,the initial values of the weights should be neither too large,nor too small.For instance,a typical range of choices of the initial weights might be something like(0.4,0.1) ∪(0.1,0.4),rather than(0.1,0.1) as suggested by the usual strategy.Our theory that medium size weights should be used has also been extended to a few commonly used transfer functions and error functions.Numerical experiments are carried out to support our theoretical findings.     

Uniform modeling of parameter dependent nonlinear systems

This paper addresses the problem of approximating parameter dependent nonlinear systems in a unified framework. This modeling has been presented for the first time in the form of parameter dependent piecewise affine systems. In this model, the matrices and vectors defining piecewise affine systems are affine functions of parameters. Modeling of the system is done based on distinct spaces of state and parameter, and the operating regions are partitioned into the sections that we call ’multiplied simplices’. It is proven that this method of partitioning leads to less complexity of the approximated model compared with the few existing methods for modeling of parameter dependent nonlinear systems. It is also proven that the approximation is continuous for continuous functions and can be arbitrarily close to the original one. Next, the approximation error is calculated for a special class of parameter dependent nonlinear systems. For this class of systems, by solving an optimization problem, the operating regions can be partitioned into the minimum number of hyper-rectangles such that the modeling error does not exceed a specified value. This modeling method can be the first step towards analyzing the parameter dependent nonlinear systems with a uniform method.     

基于像素拓展的图形分割与合成的方法研究

提出了一种新的图像分割与合成方法。对于图像的分割,提出了新的分割算法,先是手工将一些明显属于物体和明显属于背景的像素分割出来,然后创建一个小矩形区域进行像素拓展迭代,迭代到图像边界为止。对于图像的合成,采用平均法,设计了新的算法,使物体的基本颜色保持不变的同时,降低了合成图像中从源图像分割的物体与新背景的差异,并使得其饱和度、色度、强度更加自然真实。     

数字磁通门传感器的自动相位对准

三轴数字磁通门传感器信号处理电路的延时(相位延迟)及各轴延时不一致,影响数字滤波器的输出性能。提出了基于加权最小二乘法的互功率谱相位法计算延时的方法,针对该数字滤波器的特性,选择传感器的不同谐波信号对比分析相位延迟计算的精度,并设计了传感器的自动相位对准方法。实验结果表明:该计算方法精确有效,自动相位对准使滤波器输出性能提高25.89%。     

连续体结构的参数化水平集统一优化

针对传统分步式结构优化设计的不足,提出一种同时进行结构拓扑、形状和尺寸统一优化的设计方法.首先采用水平集函数描述统一的结构优化模型和几何尺寸边界,通过引入紧支径向插值基函数将结构拓扑优化变量、形状优化变量和尺寸优化变量变换为基函数的扩展系数;然后取该扩展系数为设计变量,借助一种参数的变化表达3种优化要素对结构性能的影响,将复杂的多变量优化问题变换为相对简单的参数优化问题,有利于与相对成熟的优化算法相结合提高求解效率;进一步用R函数将其融合为一个整体,构造出统一优化模型,并用最优化准则法进行求解.最后通过数值案例证明了该方法的有效性和精确性.     

pH传感器温度补偿模型研究

水质pH值在线监测对提高水产品健康养殖至关重要,本文将无线传感器网络与pH值传感器相集成,设计了pH值监测的无线传感器网络系统。为克服pH值测量过程中温度对测量结果的影响,提出了一种pH传感器温度补偿模型,该模型通过pH传感器和PT100铂电阻温度传感器分别测量溶液的pH值和温度,利用最小二乘法对pH值和电压进行线性分析,建立pH传感器的温度补偿模型。实验结果表明,该补偿模型测量精度高,能够实现较精确的pH值在线监测。     

全局优化的一类新的F-C函数

针对求解全局优化问题,有很多种求解方法。文中提出了一种快速求解一般无约束最优化问题的辅助函数方法。即F-C函数方法。该方法与填充函数法和跨越函数法相比较,既有相同点又有不同点。F-C函数法最大的优点就是在极小化F-C函数阶段中只需要进行一次局部极小化算法就能得到比当前极小值更低的目标函数局部极小点。文中在无Lipschitz连续的条件下,给出了一类新的求解全局优化问题的F-C函数。文中讨论了该F-C函数的优良性质并对该函数设计了相应的算法。最后,通过数值试验表明该F-C函数方法具有有效性和可行性。     

智能视频监控中运动目标检测的研究

针对某武器试验中背景复杂,现有的背景差分法在背景模型的维持和更新不能用于长期和复杂的场景,以及对近地目标提取检测困难的问题,提出一种改进的背景差分法。该算法采用结合邻域信息的背景差分法和最大方差阈值法,能够在一定程度上减小背景滞后更新引起的运动目标检测误差,且使目标边缘提取更加明确,从而提高了系统的运行速度,实现复杂背景下的运动目标检测。在Visual C＋＋6.0中用OpenCV实现了相关算法的设计,并给出了完成系统任务所需的部分关键代码,实现了运动目标和试验场景的分离与提取。仿真实验验证了该算法的有效性以及实时性。     

基于递推技术的算法设计方法的应用研究

算法是计算机科学的核心,算法设计对于开发正确、高效的程序至关重要。基于递推技术的算法设计方法通过形式化推导保证算法的可靠性,同时能较好地提高算法程序的效率。本文通过两个实例,详细介绍基于递推技术的算法设计方法形式化推导算法程序的过程。     

基于项目教学法的后期制作实践教学改革与探索

在介绍项目教学法和"后期制作"课程特点的基础上,分层次、分步骤阐述了项目教学法应用于该课程教学的整体实施思路。