基于动量分数阶梯度的卷积神经网络优化方法

针对采用传统梯度下降算法训练卷积神经网络收敛速度慢的问题,提出了动量分数阶梯度下降算法.介绍了分数阶微积分的定义,并依据问题描述,通过算法推导,将整数阶梯度下降算法中的动量思想应用到分数阶梯度下降算法中,设计出动量分数阶梯度下降算法.使用测试函数验证算法的收敛性,并分析不同分数阶阶次和动量项系数对算法收敛性的影响.在三...     

递归pi-sigma神经网络的带惩罚项的梯度算法分析

传统的梯度算法存在收敛速度过慢的问题,针对这个问题,提出一种将惩罚项加到传统误差函数的梯度算法以训练递归pi-sigma神经网络,算法不仅提高了神经网络的泛化能力,而且克服了因网络初始权值选取过小而导致的收敛速度过慢的问题,相比不带惩罚项的梯度算法提高了收敛速度。从理论上分析了带惩罚项的梯度算法的收敛性,并通过实验验证了算法的有效性。     

基于PCA的GABP神经网络入侵检测方法*

为克服BP算法易陷入局部最小的缺点,同时为减少样本数据维数,提出一种基于主成分分析(PCA)的遗传神经网络方法。通过降维和去相关加快收敛速度,采用改进的遗传算法优化神经网络权值,利用自适应学习速率动量梯度下降算法对神经网络进行训练。MATLAB仿真实验结果表明,该方法在准确性和收敛性方面都优于BP算法,应用于入侵检测系统中的检测率和误报率明显优于传统方法。     

带有微分项改进的自适应梯度下降优化算法

梯度下降算法作为卷积神经网络训练常用优化算法,其性能的优劣直接影响网络训练收敛性.本文主要分析了目前梯度优化算法中存在超调而影响收敛性问题以及学习率自适应性问题,提出了一种带微分项的自适应梯度优化算法,旨在改善网络优化过程收敛性的同时提高收敛速率.首先,针对优化过程存在较大超调量的问题,通过对迭代算法的重整合以及结合传统控制学原理引入微分项等方式来克服权重更新滞后于实际梯度改变的问题;然后,引入自适应机制来应对因学习率的不适应性导致的收敛率差和收敛速率慢等问题;紧接着,基于柯西-施瓦茨和杨氏不等式等证明了新算法的最差性能上界(悔界)为■.最后,通过在包括MNIST数据集以及CIFAR-10基准数据集上的仿真实验来验证新算法的有效性,结果表明新算法引入的微分项和自适应机制的联合模式能够有效地改善梯度下降算算法的收敛性能,从而实现算法性能的明显改善.     

基于一类非线性特性的FNN 训练算法

针对ＢＰ算法收敛缓慢和易于陷入局部极小的缺点,将基于一类非线性特性的动量项引入ＢＰ算示的梯度搜索,提出前向神经网络的一种通用且简单的全局训练算法。结合升温策略,算法在优化精度和训练速度方面有较大的改善。典型算法的仿真验证了算法的有效性。     

神经网络非梯度优化方法研究进展

神经网络优化是机器学习领域的一个基础性前沿课题。相较于神经网络的纯梯度优化算法,非梯度算法在解决收敛速度慢、易陷入局部最优、无法解决不可微等问题上表现出更大的优势。在剖析基于梯度的神经网络方法优缺点的基础上,重点对部分非梯度优化方法进行了综述,包括前馈神经网络优化和随机搜索优化;从基本理论、训练神经网络的步骤以及收敛性等方面对非梯度优化方法的优缺点和应用情况进行了分析;总结了基于非梯度的训练神经网络的算法在理论和应用方面面临的挑战并且展望了未来的发展方向。     

基于动量项分离的深度学习优化算法

针对神经网络处理参数更新的优化算法中出现的局部最优点振荡问题,改进带动量项的随机梯度下降算法,提出了一种动量项分离的优化算法。通过计算当前时刻目标函数的曲率半径,根据阈值适时分离动量项,从而缓解局部最优点振荡问题。实验表明,动量项分离的优化算法能够适用于不同的模型结构和不同数据集。相较于带动量项的随机梯度下降算法,具有更高的准确度,能够更快地稳定收敛。与同类一阶动量算法相比,其准确率上升明显,为深度神经网络的参数更新提供了一种新的有效的解决方案。     

自适应动量项BP神经网络盲均衡算法

为了消除数字信号在传输过程中产生的码间串扰,使得接收端能够正确解调,对信道畸变进行有效补偿,在基于动量项BP神经网络盲均衡算法的基础上,提出一种能够自适应调节BP神经网络动量项的盲均衡算法.该算法根据盲均衡过程中误差函数的变化情况,自适应调节BP神经网络的动量项,充分发挥动量项在避免网络训练陷于较浅的局部极小点的优势.仿真实验结果表明,该算法在稳定性及收敛性能上均优于固定动量BP神经网络盲均衡算法.     

反向传播神经网络收敛性的探讨

标准BP算法采用的最陡梯度下降法使得均方误差达到最小的策略可能存在两大问题：①陷入局部最小而没有收敛到全局最小,即不收敛;②收敛速率慢。本文从训练算法角度方面,比较了标准BP算法、动量算法、可变学习速率算法和Levenberg-Marquardt算法这几种方法的收敛性以及收敛速率,并通过Matlab仿真进行了验证。     

动量滤波-eLMS算法及其性能分析

针对有源噪声控制中滤波-e LMS(最小均方算法)算法收敛速度慢,收敛步长取值范围小及受参考信号自相关矩阵特征值分散程度影响较大的缺点,提出一种改进的滤波-e LMS箅法一动量滤波-e LMS算法.算法在滤波-e LMS算法的基础上,结合动量LMS算法,在权系数更新迭代时引入一个动量项,此动量项包含了先前梯度的估计值.理论推导证明算法不仅可以加快系统的收敛速度还可以扩大收敛因子的取值范围.仿真结果表明,动量滤波-e LMS算法具有收敛速度快、稳态误差小的优点.还讨论了算法中不同动量因子对算法收敛性能的影响,确定了它们的最优取值范围.     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

基于QTE的蒙古文显示机制研究与实现

蒙古语言是中国蒙古族使用的通用语言,由于蒙古文区别于其他文字的书写方式和其自身变形机制等特点,在很多通用的文字处理引擎中都不被支持。在嵌入式产品开发与应用领域中Linux加QTE已经成为流行方式。该文给出了一种在QTE环境上实现基于标准Unicode的蒙古文点阵显示和变形算法, 并自定义了支持蒙古文的QTE组件,扩展了QTE功能,为在Linux加QTE方式的嵌入式体系结构中处理蒙古文提供了一种解决方法。