基于改进遗传算法优化BP神经网络的糖尿病并发症预测模型#br#

BP神经网络是在深度学习的研究中使用较为频繁的神经网络。本文提出一种改进遗传算法优化BP神经网络的算法（IGABP）,利用遗传算法的全局搜索能力优化BP神经网络的初始结构。由于遗传算法易陷入局部最优解,影响自身的寻优能力,故对遗传算法进行改进,最后构建糖尿病并发症预测模型进而预测糖尿病并发症的发生。本文改进遗传算法的选择算子并改进自适应遗传算法的交叉及变异概率公式。通过构建预测模型,将改进后的IGABP与BP、GABP、AGABP进行比较。仿真实验结果表明,使用IGABP进行预测的准确率要明显优于BP、GABP与AGABP,并且加快了网络的收敛速度。     

基于粒子群优化的灰色神经网络组合预测模型研究

灰色神经网络在人工智能预测领域已经得到广泛的应用,但由于其自身存在局部最小化和收敛速度慢等问题,使其预测精度受到一定的限制。针对其不足,本文提出一种利用粒子群算法优化BP神经网络的学习算法,在此基础上,利用灰色预测方法对股指期货历史数据进行初步预测,并且把初步预测的结果作为优化BP神经网络的输入进行训练和预测,构建了基于粒子群优化的灰色神经网络组合预测模型(PSO-GMNN)。仿真实验结果表明,新预测模型的预测精度高于BP神经网络、灰色神经网络和灰色预测模型,同时也表明了该方法的有效性和可行性。     

MATLAB神经网络BP网络研究与应用

阐述了MATLAB神经网络，着重研究了其BP网络的网络结构，指出了BP算法的主要缺点，利用其工具箱中的函数对BP算法进行了改进。根据MATLAB神经网络BP网络的网络结构，提出了一种具有天气敏感性的基于快速BP算法的神经网络预测模型，并对电力短期负荷进行了预测。预测结果，证明了该算法的有效性。     

基于BASFPA-BP的可靠性预测模型研究

软件可靠性预测以软件可靠性预测模型为基础，对软件的可靠性以及与其直接相关的度量进行分析、评价和预测，利用软件运行中所收集的失效数据对未来的软件可靠性进行预测，成为了评估软件失效行为和保障软件可靠程度的重要手段。BP神经网络结构简单、参数少、易实现，在软件可靠性预测领域已经得到了广泛应用。然而基于传统BP神经网络搭建的软件可靠性预测模型的预测精度无法达到预期目标，因此提出了基于BASFPA-BP的软件可靠性预测模型。该模型利用软件失效数据，在BP神经网络训练过程中利用BASFPA算法优化网络权值、阈值，从而提高模型的预测精度。选用3组公开的软件失效数据，将实际值与预测值的均方误差作为预测结果的衡量标准，同时将BASFPA-BP与FPA-BP,BP,Elman这3种模型进行对比研究。实验结果表明，基于BASFPA-BP的软件可靠性预测模型在同类型模型中实现了较高的预测精度。     

基于差分进化的BP神经网络预测混沌时间序列

针对BP神经网络预测模型收敛速度慢和容易陷入局部极小值的缺点,将差分进化算法和神经网络结合起来,提出了一种基于差分进化算法的BP神经网络预测混沌时间序列的方法,利用差分进化算法的全局寻优能力对BP神经网络的权值和阈值进行优化,然后训练BP神经网络预测模型求得最优解,将该预测方法用到3个典型的混沌时间序列进行算法的有效性验证,并与BP算法的预测精度进行了比较,仿真结果表明该方法对混沌时间序列预测具有更好的非线性拟合能力和更高的预测准确性。     

改进人工蜂群算法优化RBF神经网络的短时交通流预测

为了提高径向基函数RBF神经网络预测模型对短时交通流的预测准确性,提出了一种基于改进人工蜂群算法优化RBF神经网络的短时交通流预测模型。利用改进人工蜂群算法确定RBF网络隐含层的中心值以及隐含层单元数,然后训练改进的人工蜂群算法RBF神经网络预测模型,并将其应用到某城市4天的短时交通流量数据的验证。将实验结果与传统RBF神经网络预测模型、BP神经网络预测模型和小波神经网络预测模型进行了比较。对比结果表明,该方法对短时交通流具有更高的预测准确性。     

ACO-BP神经网络在电梯交通流预测中的应用

电梯交通流预测为电梯配置与群控调度提供必要的乘客流数据信息.针对基于BP神经网络的电梯交通流预测模型在网络训练过程中表现出的对初值敏感、易陷入极小值等问题,提出利用全局寻优的蚁群优化(ACO)算法优化BP神经网络.同时,利用精英蚂蚁和排序策略对基本ACO算法进行改进.采集天津某办公大厦实际交通流数据进行实例分析,分别对基于传统的BP神经网络和蚁群优化的BP(ACO-BP)神经网络的电梯交通流预测模型进行仿真验证.结果表明:ACO-BP神经网络的预测效果远优于传统的BP神经网络,适用于电梯交通流预测系统.     

基于交变粒子群BP网络的电力系统短期负荷预测

短期负荷预测是电力系统正常运行的关键环节,合理的发电计划依靠准确的负荷预测,因此提出交变粒子群算法来优化BP网络模型以预测电力短期负荷。针对 依靠先前的经验 来确定BP神经网络的权值缺少理论依据的问题,采用交变粒子算法优化BP神经网络权值,以减少通过神经网络预测模型求解电力短期负荷预测带来的误差。实验证明,经过优化的BP神经网络预测模型比传统的BP神经网络预测模型的误差更小,更加接近实际电力负荷。     

基于遗传算法和BP神经网络的房价预测分析

针对BP神经网络存在易陷入局部极小值、收敛速度慢等问题,提出用遗传算法优化BP神经网络并用于房价预测。采用BP神经网络建立房价预测模型。利用遗传算法对BP神经网络的初始权值和阈值进行优化。选取1998年2011年贵阳市的房价及其主要影响因素作为实验数据,分别对传统的BP神经网络和经过遗传算法优化后的BP神经网络进行训练和仿真实验,结果表明,与传统的BP神经网络预测模型相比,经过遗传算法优化后的BP神经网络预测模型能加快网络的收敛速度,提高房价的预测精度。     

QPSO算法优化BP网络的网络流量预测

网络流量预测对于大规模网络的规划设计和网络资源管理等方面都具有积极的意义,是网络流量工程重要组成部分。结合QPSO算法和BP神经网络的优势,采用QPSO算法对BP神经网络的权值和阈值进行优化,并利用历史记录训练BP网络。仿真实验表明,与PSO训练的BP网络以及直接用BP网络进行预测的模型相比,基于QPSO训练的BP网络流量预测模型具有更好的预测能力。     

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.