自学习软件自动化系统NDSAIL的设计与实现

本文介绍一个具有自学习功能的软件自动化系统NDSAiL。该系统能自动从示例中学习问题分解方法,并用于自动生成部分通用算法.该系统还能自动学习基本算法及算法优化方法.在算法构架学习背景下,有效地解决了解释学习的操作性准则问题;并针对一类问题解决了归纳学习结果的正确性问题以及unfold/fold转换技术的完全自动化问题.     

Z规格说明的前置条件的简化

在软件方法学中，形式方法越来越受到人们的重视，并已被应用于软件开发．Z是一种基于数学表示的软件规格说明方法．前置条件的简化是Z规格说明方法中一种标准的检查，本文讨论了Z规格说明中关于操作的前置条件及其计算．提出了简化过程的终止条件，给出了一个用于简化前置条件的算法，该算法可自动产生简化过程的证据．     

径向基函数神经网络的新型混合递推学习算法

从径向基函数网络的硬件实现和实时应用的角度出发，给出了RBF网络的一种新型混合递推学习算法．该算法既具有良好的数值性质又易于并行实现．把RBF网络用于非线性系统在线辨识，仿真结果显示了本文方法的有效性．     

一种吸引分岔知识同模型及其应用实例

本文基于非线性动力学，特别是托姆的形态发生学思想，针对视觉学习，给出了一种吸引分岔知识网模型，用于解决知识表示和获取问题．通过引入皮亚杰发生认识论中的概念，模型拥有的学习功能包括强化、同化、顺应、聚合、分裂和遗忘；这样就给出了一个学习视觉知识的完整方法．３个应用系统的结果表明，该模型及其学习方法，对于解决实际问题，是有效和适用的．     

用自调整S函数提高神经网络BP算法

学习算法是BP前馈神经网络研究中的核心问题。文中阐述了几种重要的BP算法的改进算法，提出了一种新的学习算法，即自动调整S型函数形状的算法，从理论上证明了此方法在一定前提条件下的可行性，给出了进行S型函数形状自动调整的公式。仿真实验证明了新学习算法用于非线性系统建模时的有效性。     

一个适应性的人工免疫系统的软件构架

适应性是人工免疫系统（AIS，Artificia lImmune System）的重要特性之一。在AIS软件开发应用中，数据源的进化和学习算法的进化是两个有复杂关联的适应性问题。为此我们扩展并改进了已有的AIS构架，提出一个新的适应性软件构架。该构架以基因计算为中心，扩展了元基因来适应数据源的进化，并设计了可接入学习算法构件和算法验证机制来解决算法进化的适应性问题。在该构架支持下，数据源的进化独立于学习算法的设计，同时使学习算法能适用于多种数据源且能独立进化。该构架可简化AIS软件的复杂性，可提高AIS开发应用的效率，也有助于实现将来的自适应的免疫计算。     

基于问题模式的形式化软件规格说明生成方法

精确的形式化软件规格说明是软件描述、开发与验证的基础,而工业界普遍使用非(半)形式化的表示定义与描述用户需求,如何由非(半)形式化的用户需求生成形式化软件规格说明是需求工程的难点之一.将设计模式的概念进行扩展,定义了问题模式,提出了一种基于问题模式形式化软件规格说明生成方法.该方法从结构化自然语言SNL描述的高层问题需求出发,通过选择知识库中的问题模式逐步精化得到各个新的子问题对应的形式化规格说明,之后对各个子问题组合并进行优化以得到最终的形式化规格说明.进一步,使用模型精化演算的原理与概念给出了该生成方法的理论基础.采用算法程序领域作为研究对象并使用Radl语言作为形式化规格说明语言.通过算法程序领域中的典型实例对这一方法进行了详细的描述,实际效果表明该方法能有效地生成高质量形式化规格说明.     

Uppaal到PVS的时间自动机翻译器

Uppaal是一种对实时系统模型进行建模和验证的工具,PVS（Prototype Verification System）是开发和分析形式化规格说明的原型证明系统。介绍了Uppaal2PVS翻译器的设计与实现,给出了一种将用Uppaal生成的时间自动机规格说明翻译成PVS文件的方法,从而将模型检查问题转换成了定理证明问题,解决了潜在的状态空间爆炸问题。最后给出了一个实例。     

组合优化多智能体进化算法

提出了一种新的组合优化方法——组合优化多智能体进化算法．该方法将智能体固定在网格上，而每个智能体为了增加自身能量将与其邻域展开竞争，同样智能体也可进行自学习来增加能量．理论分析证明算法具有全局收敛性．在实验中，作者分别用强联接、弱联接、重叠联接等各种类型的欺骗函数对算法的性能进行了全面的测试，并将算法用于解决具有树状等级结构的问题．比较结果表明文中算法所需的计算量远远小于其它方法，具有较快的收敛速度．为了测试算法解决大规模问题的能力，作者还将算法用于解决上千维的欺骗问题和等级问题，结果表明该文算法的计算复杂度与问题规模成多项式的关系．此外，将算法用于上千维的欺骗问题和等级问题，在国内外还均未见报到．     

反应块及其在类比推理中的应用

本文讨论了类比在问题求解中的应用问题．给出了应用反应块识别类比源以及自动生成反应块的算法．本文还给出了一种解法序列分割方法，用于类比源的获取及存储．这些思想和方法已在符号积分求解与学习系统ＩＳＬＳ（ｉｎｔｅｇｒａｔｉｏｎｓｏｌｖｉｎｇａｎｄｌｅａｒｎｉｎｇｓｙｓｔｅｍ）中实现．     

无线传感器网络中基于规范的入侵检测算法研究

为了解决无线传感器网络的安全问题,提出了一种基于规范的入侵检测算法。该算法根据概率论的有关理论,对邻域节点的单位时间特征量设定阈值,阈值的设定方法具有通用性,并且阈值自适应更新,符合传感节点性能随着时间发生变化的特点。将检测节点获得的单位时间特征量值与阈值进行对比来判断入侵。通过仿真实验验证了该算法优于其他基于规范的入侵检测方案,不仅能够满足高检测率低误报率的要求,还具有较好的能效性。     

非线性迭代学习控制问题的延拓修正牛顿法

对于非线性迭代学习控制问题,提出基于延拓法和修正Newton法的具有全局收敛性的迭代学习控制新方法.由于一般的Newton型迭代学习控制律都是局部收敛的,在实际应用中有很大局限性.为拓宽收敛范围,该方法将延拓法引入迭代学习控制问题,提出基于同伦延拓的新的Newton型迭代学习控制律,使得初始控制可以较为任意的选择.新的迭代学习控制算法将求解过程分成N个子问题,每个子问题由换列修正Newton法利用简单的递推公式解出.本文给出算法收敛的充分条件,证明了算法的全局收敛性.该算法对于非线性系统迭代学习控制具有全局收敛和计算简单的优点.     

多变量非线性系统的变阶采样迭代学习控制

针对存在初态误差的情形, 提出多变量非线性系统的变阶采样迭代学习控制方法. 相对固定阶迭代学习算法, 变阶算法可有效降低跟踪误差. 对变阶采样迭代学习算法进行了收敛性分析, 推导出收敛充分条件. 给出了变阶学习的两种实现策略-DD (Direct division)和DIP (Division in phases)策略. 数值仿真表明, 基于DIP策略的变阶采样迭代学习算法在获得较高的控制精度的同时, 具有较快的收敛速度.     

Recursive Direct Learning of Control Efforts for Trajectories with Different Magnitude Scales

Direct learning control (DLC) schemes have been developed recently to address non‐repeatable trajectory tracking problems. Unlike conventional iterative learning schemes, DLC schemes learn a set of unknown basis function vectors which can be used to generate the desired control profile of a new trajectory. DLC schemes use all available trajectory tracking information to obtain the unknown basis function vectors in a Least Squares and pointwise manner. A drawback of DLC is that the inverse matrix calculation is inevitable, which is time consuming and may result in singularities due to the batch processing nature. A Recursive Direct Learning Control method is proposed which learns the basis function vectors meanwhile overcomes the implementation difficulties in DLC schemes. The focus of this paper is on learning the control profile of trajectories with same operation period but different magnitude scales. The recursive learning method makes use of one trajectory information at a time, thus avoids the batch processing. The scheme is first developed for a class of nonlinear time varying systems and then extended to cover more general classes of nonlinear systems including robotic manipulator dynamics. Extensive simulation results on a two‐link robotic model are provided to confirm the features of the proposed algorithm.     

基于泛化竞争和局部渗透机制的自组织网TSP问题求解方法

旅行商问题(TSP)是组合优化中最典型的NP完全问题之一,具有很强的工程背景和应用价值.文章在分析了标准SOM(Self-Organizing Map)算法在求解TSP问题的不足和在寻求总体最优解的潜力的基础上,引入泛化竞争和局部渗透这两个新的学习机制,提出了一种新的SOM算法---渗透的SOM(Infiltrative SOM,ISOM)算法.通过泛化竞争和局部渗透策略的协同作用:总体竞争和局部渗透并举、先倾向总体竞争后倾向局部渗透、在总体竞争基础上的局部渗透,实现了在总体路径寻优指导下的局部路径优化,从而使所得路径尽可能接近最优解.通过对TSPLIB中14组TSP实例的测试结果及与KNIES、SETSP、Budinich和ESOM等类SOM算法的比较,表明该算法既简单又能使解的质量得到很大提高,同时还保持了解的良好的稳健特性.     

On tree types of competitive learning algorithms with their comparisons and applications to MRI segmentation

This paper considers competitive learning networks using three types of hard, soft, and fuzzy learning schemes. The hard competitive learning algorithm is with the winner‐take‐all. The soft competition learning algorithm is with a stochastic relaxation technique using the Gibbs distribution as a dynamic neighborhood function. The fuzzy competition learning algorithm is with a fuzzy relaxation technique using fuzzy membership functions as kernel type neighborhood interaction functions. Some numerical examples are made for these three types of competitive learning schemes. The numerical results show that the fuzzy learning has better performance than hard and soft learning under the normal mixture data. We then present an application to magnetic resonance image segmentation. A real case of ophthalmology recommended by a neurologist with MR image data is examined in this paper. These competitive learning algorithms are used in segmenting the ophthalmological MRI data for reducing medical image noise effects with a learning mechanism. Based on the segmentation results, the fuzzy learning gives better performance than hard and soft learning so that the fuzzy competitive learning algorithm is recommended for use in MRI segmentation as an aid for support diagnoses. © 2010 Wiley Periodicals, Inc.     

面向车联网的联邦学习模型定制框架及算法改进

针对车联网联邦学习服务难以满足用户训练个性化模型的需求,提出一种创新性的车联网联邦学习模型定制化服务框架。该框架采用了一种融合设备贡献度和数据集相似性的联邦学习聚合算法,实现了个性化联邦学习。该算法通过不同权重分配方式和相似性计算,使得不同用户可以根据自己的需求和数据特征,选择合适的模型训练方案。该框架还提出了一种双重抽样验证方法,解决了模型性能和可信度问题;此外,利用智能合约支持数据协作,保障了数据的安全性。实验结果表明,提出算法在大多数实验场景中表现出较高的准确率,该框架可以显著提高车联网服务的个性化水平,同时保证模型的准确性和可靠性。     

On-line EM Variants for Multivariate Normal Mixture Model in Background Learning and Moving Foreground Detection

The unsupervised learning of multivariate mixture models from on-line data streams has attracted the attention of researchers for its usefulness in real-time intelligent learning systems. The EM algorithm is an ideal choice for iteratively obtaining maximum likelihood estimation of parameters in presumable finite mixtures, comparing to some popular numerical methods. However, the original EM is a batch algorithm that works only on fixed datasets. To endow the EM algorithm with the capability to process streaming data, two on-line variants are studied, including Titterington’s method and a sufficient statistics-based method. We first prove that the two on-line EM variants are theoretically feasible for training the multivariate normal mixture model by showing that the model belongs to the exponential family. Afterward, the two on-line learning schemes for multivariate normal mixtures are applied to the problems of background learning and moving foreground detection. Experiments show that the two on-line EM variants can efficiently update the parameters of the mixture model and are capable of generating reliable backgrounds for moving foreground detection.     

Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression

This article develops an evolutional fuzzy particle swarm optimization (FPSO) learning algorithm to self extract the near optimum codebook of vector quantization (VQ) for carrying on image compression. The fuzzy particle swarm optimization vector quantization (FPSOVQ) learning schemes, combined advantages of the adaptive fuzzy inference method (FIM), the simple VQ concept and the efficient particle swarm optimization (PSO), are considered at the same time to automatically create near optimum codebook to achieve the application of image compression. The FIM is known as a soft decision to measure the relational grade for a given sequence. In our research, the FIM is applied to determine the similar grade between the codebook and the original image patterns. In spite of popular usage of Linde–Buzo–Grey (LBG) algorithm, the powerful evolutional PSO learning algorithm is taken to optimize the fuzzy inference system, which is used to extract appropriate codebooks for compressing several input testing grey-level images. The proposed FPSOVQ learning scheme compared with LBG based VQ learning method is presented to demonstrate its great result in several real image compression examples.     

On neurobiological, neuro-fuzzy, machine learning, and statisticalpattern recognition techniques

In this paper, we propose two new neuro-fuzzy schemes, one for classification and one for clustering problems. The classification scheme is based on Simpson's fuzzy min-max method (1992, 1993) and relaxes some assumptions he makes. This enables our scheme to handle mutually nonexclusive classes. The neuro-fuzzy clustering scheme is a multiresolution algorithm that is modeled after the mechanics of human pattern recognition. We also present data from an exhaustive comparison of these techniques with neural, statistical, machine learning, and other traditional approaches to pattern recognition applications. The data sets used for comparisons include those from the machine learning repository at the University of California, Irvine. We find that our proposed schemes compare quite well with the existing techniques, and in addition offer the advantages of one-pass learning and online adaptation.