修正高斯模型神经网络的色谱重叠峰解析

目的：研究色谱重叠峰解析新方法。方法：构造了以修正高斯模型（EMG）为基函数的径向基函数神经网络（EMG＿RBFNN）,在网络学习算法中提出采用基于共享小生境技术的约束最优保留两阶段遗传算法：结构学习和参数最速梯度下降进化,从而使EMG＿RBFNN具有结构学习能力 ,使该神经网络能够适应组分数未知的色谱重叠峰解析。尤其是在将气相和高效液相色谱EMG模型参数τ、σ和tR之间的近似线性关系作为可行域约束条件引入算法后,极大地限制了算法可行的解空间,减少了局部最优解出现的概率,提高了算法运行效率。结果：将此新方法分别应用于计算机仿真色谱和45例中药材HPLC,算法可以以较高的精度解析出仿真色谱,实际色谱峰解析也有很高的解析精度。结论：该方法非常适用于组分数未知的各种色谱（重叠）峰的解析。     

利用基于小波特征提取的网络模型解析色谱重叠峰

提出了一种新的色谱重叠峰解析模型——基于小波特征提取的RBF神经网络模型。首先利用小波变换奇异性的检测原理,从原始色谱信号中提取特征点,这些特征点蕴含了反映色谱峰形状的信息,包括重叠峰个数、保留时间等信息。由小波变换获得的特征点来确定RBF网络的隐节点数目和网络参数的初值,即将拐点对数作为隐节点数目,将峰宽估计值作为输出层连接权的初值,将峰高估计值作为隐节点宽度的初值。再用RBF网络来拟合原始重叠色谱信号,梯度下降法训练后获得的网络参数作为解析结果,实现了重叠色谱峰的分离。实验结果表明:本方法快速、准确、可靠,能有效解析未知组分数的重叠峰。     

基于量子粒子群优化算法的新型正交基神经网络分数阶混沌时间序列单步预测

针对分数阶混沌时间序列预测精度低、速度慢的问题,提出了基于量子粒子群优化(QPSO)算法的新型正交基神经网络预测模型。首先,在Laguerre正交基函数的基础上提出一种新型正交基函数,并结合神经网络拓扑构成新型正交基神经网络;其次,利用QPSO算法优化新型正交基神经网络参数,将参数优化问题转化为多维空间上的函数优化问题;最后,根据已优化参数建立预测模型并进行预测分析。分别以分数阶Birkhoff-shaw和Jerk混沌系统为模型,利用Adams-Bashforth-Moulton预估-校正法产生混沌时间序列作为仿真对象,进行单步预测对比实验。仿真表明,与反向传播(BP)神经网络、径向基函数(RBF)神经网络及普通的新型正交基神经网络相比,基于QPSO算法的新型正交基神经网络的平均绝对值误差(MAE)、均方根误差(RMSE)明显减小,决定度系数(CD)更接近于1,平均建模时间(MMT)明显缩短。实验结果表明,基于QPSO算法的新型正交基神经网络提高了分数阶混沌时间序列预测的精度和速度,便于该预测模型的应用和推广。     

用蛙跳算法优化RBF神经网络参数的研究

针对径向基函数（Radial Basis Functions,RBF）神经网络结构参数确定问题,提出了一种基于蛙跳算法优化RBF神经网络参数的新方法。将RBF神经网络参数组成一个多维向量,作为蛙跳算法中的参数进行优化。以适应度函数为标准,在可行解空间中搜索最优解,并对蛙跳算法进行了改进。非线性函数逼近实验结果表明,该优化算法相对标准遗传优化算法、粒子群优化算法有较小的均方误差,具有更好的逼近能力。     

基于QPSO算法的RBF神经网络参数优化仿真研究

针对粒子群优化(PSO)算法搜索空间有限,容易陷入局部最优点的缺陷,提出一种以量子粒子群优化(QPSO)算法为基础的RBF神经网络训练算法,将RBF神经网络的参数组成一个多维向量,作为算法中的粒子进行进化,由此在可行解空间范围内搜索最优解。实例仿真表明,该学习算法相比于传统的学习算法计算简单,收敛速度快,并由于其算法模型的自身特性比基于PSO的学习算法具有更好的全局收敛性能。     

基于QPSO-RBF NN的混沌时间序列预测

提出一种基于量子粒子群优化算法训练径向基函数神经网络进行混沌时间序列预测的新方法.在确定径向基函数网络的隐层节点数后,将相应网络的参数,包括隐层基函数中心、扩展常数,以及输出权值和偏移编码成学习算法中的粒子个体,在全局空间中搜索具有最优适应值的参数向量.实例仿真证实了该方法的有效性.     

自寻优自适应动态面控制

针对一类不确定非线性系统的跟踪问题,利用神经网络和动态面技术设计控制器,提出一种控制器参数自寻优策略.在每个子系统中,应用径向基函数(RBF)神经网络逼近该子系统中的不确定项;在每一步递推中,引入一个滤波器以克服反推技术中控制项爆炸的缺点.通过定义一个优化目标函数,应用梯度法在控制器参数可行解中寻找一组最优的控制器参数.数值仿真表明该方案是可行的.     

广义模糊推理与广义模糊RBF 神经网络

在提出广义模糊推理概念的基础上,提出并分析了广义模糊径向基(RBF)神经网络模型,给出了该网络的广义学习算法。仿真结果验证了广义模糊RBF神经网络模型具有良好的函数逼近能力,所提出的学习算法是可行和有效的。     

改进的RBFNN在运动员竞技状态预测中的应用

提出了一种改进的径向基函数(RBF)神经网络,该神经网络以模糊系统模型为基础。首先利用减法聚类算法确定径向基函数的中心数,然后通过模糊C均值聚类算法优化基函数中心与宽度,最后依据样本数据的聚类结果设计RBF神经网络并进行训练。将该神经网络应用于网球队运动员的竞技状态的预测。仿真结果表明:该算法先进有效、具有较高的精度,用其建立的模型具有较强的实用性。     

基于混合禁忌搜索算法的重叠色谱峰解析

结合模拟退火算法中的Metropolis准则和免疫进化算法巾产生优秀子代的思想,提出一种混合禁忌搜索算法,并根据指数修正的Gauss卷积模型,使用该算法解析计箅机模拟谱图和实验谱图中的重叠峰,结果该算法用于解析重叠色谱峰性能稳定,精度较高.     

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。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.