Ridge Polynomial神经网络带动量项异步梯度算法的收敛性

将动量项引入到Ridge Polynomial神经网络异步梯度训练算法的误差函数中,有效地改善了算法的收敛效率,并从理论上分析了Ridge Polynomial神经网络的带动量项的异步梯度算法的收敛性,给出了算法的单调性和收敛性(包括强收敛性和弱收敛性)。算法的这些收敛性质对于如何选取学习率和初始权值来进行高效的网络训练是非常重要的。最后通过计算机仿真实验验证了带动量项的异步梯度算法的高效性和理论分析的正确性。     

随机ADD算法的不确定网络优化研究

传统的网络优化问题通过对偶梯度下降算法来解决,虽然该算法能够以分布式方式来实现,但其收敛速度较慢.加速对偶下降算法(ADD)通过近似牛顿步长的分布式计算,提高了对偶梯度下降算法的收敛速率.但由于通信网络的不确定性,在约束不确定时,该算法的收敛性难以保证.基于此,提出了一种随机形式的ADD算法来解决该网络优化问题.理论上证明了随机ADD算法当不确定性的均方误差有界时,能以较高概率收敛于最优值的一个误差邻域;当给出更严格的不确定性的约束条件时,算法则可以较高概率收敛于最优值.实验结果表明,随机ADD算法的收敛速率比随机梯度下降算法快两个数量级.     

面向异构网络的联邦优化算法

联邦学习网络中,全局模型的聚合训练常因边缘设备端的统计异构性而存在收敛问题。针对高度异构环境的适应性问题,提出一种面向异构网络的联邦优化算法q-FedDANE。首先,通过在经典联合近似牛顿型方法中引入衰减参数q,调整衰减梯度校正项和近端项的负面影响,有效提高模型对环境异构性的感知能力,并将每轮算法迭代的设备通信轮次降低至一次,显著减少通信成本和训练开销;其次,模型将随机优化器Adam引入服务器端聚合训练,通过自适应的动态学习率来利用全局信息进行目标优化,加快了模型的收敛速度。实验表明,q-FedDANE算法可以更好地适应环境异构和低设备参与的场景,在高度异构的FEMNIST数据集上,该算法最终获得的测试精度约高出FedDANE的58%。     

基于PSO算法的模糊神经网络的网络异常检测

在网络异常检测中,为了提高对异常状态的检测率,降低对正常状态的误判率,提出一种基于粒子群优化算法训练模糊神经网络进行网络异常检测的新方法。在对模糊神经网络训练中采取PSO算法和梯度下降算法相结合的方法,充分发挥PSO全局寻优的能力和梯度下降局部细致搜索优势。实验数据采用KDD CUP99数据集,实验结果表明,该学习算法与传统的梯度下降法（GD）相比,收敛速度快,具有更好的全局收敛性,提高了异常检测的准确性,同时该方法对于新的异常也有较高检测率。     

基于改进萤火虫算法的小波神经网络短期负荷预测方法

传统的小波神经网络以梯度下降法训练网络,而梯度下降法易导致网络出现收敛早熟、陷入局部极小等问题,影响网络训练的精度。文章将萤火虫算法用于训练小波神经网络,在全局内搜寻网络的最优参数。为了提高萤火虫算法参数寻优的能力,在训练过程中自适应调节γ值。同时利用高斯变异来提高萤火虫个体的活性,在保证收敛速度的同时避免算法陷入局部极小。将优化后的小波神经网络用于短期负荷预测,实验证明改进后的预测模型非线性拟合能力较强、预测精度较高。     

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。     

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。     

自适应增强卷积神经网络图像识别

目的 为了进一步提高卷积神经网络的收敛性能和识别精度,增强泛化能力,提出一种自适应增强卷积神经网络图像识别算法。方法 构建自适应增强模型,分析卷积神经网络分类识别过程中误差产生的原因和误差反馈模式,针对分类误差进行有目的地训练,实现分类特征基于迭代次数和识别结果的自适应增强以及卷积神经网络权值的优化调整。自适应增强卷积神经网络与多种算法在收敛速度和识别精度等性能上进行对比,并在多种数据集上检测自适应卷积神经网络的泛化能力。结果 通过对比实验可知,自适应增强卷积神经网络算法可以在很大程度上优化收敛效果,提高收敛速度和识别精度,收敛时在手写数字数据集上的误识率可降低20.93%,在手写字母和高光谱图像数据集上的误识率可降低11.82%和15.12%;与不同卷积神经网络优化算法对比,误识率比动态自适应池化算法和双重优化算法最多可降低58.29%和43.50%;基于不同梯度算法的优化,误识率最多可降低33.11%;与不同的图像识别算法对比,识别率也有较大程度提高。结论 实验结果表明,自适应增强卷积神经网络算法可以实现分类特征的自适应增强,对收敛性能和识别精度有较大的提高,对多种数据集有较强的泛化能力。这种自适应增强模型可以进一步推广到其他与卷积神经网络相关的深度学习算法中。     

雷达目标检测深层自编码器自适应优化算法

研究了现阶段雷达低小慢目标探测技术的难点与方法。分析了深层自编码器基本模型与算法，通过引入自适应学习理论，提出了基于Rumelhart函数的深层自编码器自适应算法（RDAAA），并证明了算法的收敛性。优化算法避免了网络训练过程中出现惩罚过度的现象，克服了学习速率过高导致网络振荡发散，或学习速率过小降低网络收敛速度等缺陷。利用两种数据集对RDAAA、基于交叉熵函数的深层自编码器学习算法（CDAA）与误差反向传播算法（BPA）进行模式识别能力分析，结果表明在确定限定误差与选取最佳学习速率的情况下，RDAAA相对于CDAA与BPA收敛速度最快，正确识别率更高。围绕雷达目标检测与深度学习理论，分析了低小慢目标特性，将目标检测问题转化为模式分类问题，利用上述三种算法进行目标检测仿真实验，结果表明RDAAA与CDAA的性能明显优于BPA，且RDAAA的检测率更高，特别是处于低信噪比阶段，仍可保持较高的发现概率。     

基于隐式随机梯度下降的恶意代码分类算法

针对目前基于深度学习的恶意代码分类算法易出现灾难性遗忘导致分类准确率不高、收敛过慢的问题，提出基于隐式随机梯度下降的恶意代码分类算法。与现有算法不同，该算法构造内外循环网络结构来协同学习最优网络模型以提高恶意代码分类准确率。在内循环优化阶段，通过优化带有偏好正则项的损失函数迫使内循环网络沿外循环网络方向更新权重从而避免内循环网络遗忘过去学到的知识。在外循环优化阶段，通过求解近似外循环网络梯度并利用隐式随机梯度下降优化外循环网络权重，使得外循环网络能够更快更稳定地收敛。在三个恶意代码数据集上的实验结果表明，该算法有效避免了灾难性遗忘，使用较少训练轮数取得了最高的分类准确率，显著提升了恶意代码分类的稳定性和鲁棒性。     

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。     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

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.     

View-based model-driven architecture for enhancing maintainability of data access services

In modern service-oriented architectures, database access is done by a special type of services, the so-called data access services (DAS). Though, particularly in data-intensive applications, using and developing DAS are very common today, the link between the DAS and their implementation, e.g. a layer of data access objects (DAOs) encapsulating the database queries, still is not sufficiently elaborated, yet. As a result, as the number of DAS grows, finding the desired DAS for reuse and/or associated documentation can become an impossible task. In this paper we focus on bridging this gap between the DAS and their implementation by presenting a view-based, model-driven data access architecture (VMDA) managing models of the DAS, DAOs and database queries in a queryable manner. Our models support tailored views of different stakeholders and are scalable with all types of DAS implementations. In this paper we show that our view-based and model driven architecture approach can enhance software development productivity and maintainability by improving DAS documentation. Moreover, our VMDA opens a wide range of applications such as evaluating DAS usage for DAS performance optimization. Furthermore, we provide tool support and illustrate the applicability of our VMDA in a large-scale case study. Finally, we quantitatively prove that our approach performs with acceptable response times.     

A pragmatic multi-method investigation of discrepant technological events: Coping,attributions, and ‘accidental’ learning

Discrepant technological events or situations that entail a problem, a misunderstanding or a difficulty with the Information Technology (IT) being employed, are common in the workplace, and can lead to frustration and avoidance behaviors. Little is known, however, about how individuals cope with these events. This paper examines these events by using a multi-method pragmatic approach informed by coping theory. The results of two studies – a critical incident study and an experiment – serve to build and test, respectively, a theoretical model that posits that individuals use a variety of strategies when dealing with these events: they experience negative emotions, make external attributions, and adopt engagement coping strategies directed at solving the event, eventually switching to a disengagement coping strategy when they feel they have no control over the situation. Furthermore, users’ efforts may result in ‘accidental’ learning as they try to overcome the discrepant IT events through engagement coping. The paper ends with a discussion of the results in light of existing literature, future opportunities for research, and implications for practice.