基于多Agent的协同设计环境下一种新的产品开发建模方法

针对企业工作流程的产品设计的特点，综合考虑全局Agent与局部Agent之间的关系，提出了基于基于多Agent的工作流方式下的产品开发建模方法，将此方法应用到协同设计环境中，并分析了Agent在一种改善的CSCW环境下的运行状态及处理过程。     

一种面向产品线的特征依赖建模方法

特征依赖建模是描述特征间相互约束的模型,是软件产品线开发中的一项关键活动。引入了特征局部依赖和全局依赖关系,在对特征依赖关系分析的基础上,提出了一种特征依赖建模方法,该方法不仅支持分解、泛化等特征局部依赖描述,还支持配置依赖、运行依赖和影响依赖等全局依赖建模。通过一个空调控制系统的产品线特征依赖建模实例验证了该方法的有效性。     

面向对象的机器人仿真与监控系统

通过对机器人的几何建模和运动学建模进行了研究，论述了使用VC6．0语言作为开发平台，以OpenGL三维显示技术完成三维可视化，以VRML作为外部机器人模型表示，实现运行时可交互的机器人三维实时图形仿真系统的开发技术与方法。系统通过网络与机器人控制器进行通信，实时接收机器人控制器发来的状态数据，将它们动态地以三维模拟方式显示，用户能够动态地监视机器人的运动状态，在必要时对机器人的动作进行控制，为其它机器人的研究提供通用的仿真与模拟平台。     

基于MQ径向基序贯近似建模的联合优化方法

针对研究控制系统问题,对高精度模型进行全局优化,往往收敛速度慢,计算成本高。为了提高控制系统模型精度,提出了结合全局和局部的联合优化方法。利用Multiquadric（MQ）径向基插值构造近似模型,以全局优化方法对近似模型的优化解作为初值,然后使用局部优化方法对原问题进行优化。在建模过程中,详细讨论了MQ径向基函数的特点,分析了病态矩阵及形状参数的影响,并提出采样点间距离控制的方法。测试函数表明,MQ径向基函数插值的近似误差较小,联合优化方法减少了高精度模型的运行次数,收敛速度快。在应用中,能够大幅度节省计算成本。     

基于组件的集成建模与智能优化软件平台设计

针对复杂工业生产过程对通用优化软件的迫切需求，基于组件技术设计开发了集成建模与智能优化软件平台（IMIOP），实现了各种建模算法。通过多种模式集成，完成了对不同工业过程的建模和仿真，并在此基础上采用多种智能优化算法实施操作优化。该文介绍了IMIOP系统的工作流程，从组件设计的角度详细介绍了系统的总体构架和组件划分，以线性回归建模算法为例说明了COM组件设计方法。最后，通过运行实践验证了IMIOP系统的性能。     

基于PCA的密封油温异常故障诊断方法

密封油温度波动情况与汽轮发电机组安全、稳定与运行状态息息相关，为保证机组稳定运行，提出了一种基于PCA的方法对密封油温度进行实时监测系统。利用PCA算法训练进行离线建模，利用综合指标作为故障检测指标实现在线监测，最后采用基于重构的贡献图法对测试集进行故障诊断，以确保系统第一时间锁定故障参数。通过某1000 MW机组密封油空侧回路作为研究对象，构建PCA模型，结果表明，提出的方法能够准确检测密封油温度出现异常的情况，并且能在故障发生前进行提前预警，确定故障发生参数，保证机组的稳定安全运行。     

基于进化策略的板材回弹优化建模研究

以回弹最小为目标提出了一种有效的成形工艺优化方法.通过有限元方法对回弹过程进行建模和分析,以获得不同成形工艺条件下的回弹量作为神经网络的样本信号.利用RBFN来模拟复杂的回弹过程.采用改进的进化策略(ES)算法对已建立的回弹模型进行优化以获得最小回弹.结果表明,提出的RBF网络与ES相结合的方法具有全局搜索特性,对于存在不可微的目标函数的非线性优化问题,能以较快的速度和较大概率收敛于全局最优解.     

结构未建模系统的变结构自适应控制器设计

本文对一类结构未建模的动态不确定系统，采用变结构控制与自适应控制相结合的方法，给出了一种鲁棒控制器设计方案。所提方案适用于系统已建模部分为非最小相位的系统，并能保证系统输入输出全局有界稳定。     

基于交互关系分组建模融合的组群行为识别算法

组群成员间的交互关系建模是组群行为识别的核心技术。本文为解决复杂场景下组群关系繁琐、关系推理时复杂度高并存在信息冗余等问题,提出一种交互关系分组推理的模型。首先，利用CNN网络和RoIAlign提取视频帧中的场景信息和个人信息作为初始特征,利用个人空间坐标对人群进行二分组（例如：在Volleyball数据集中，利用参与者的bounding boxes的X坐标信息进行排序，然后为每个人建立序号ID,并从左到右将12名成员分为2组）；其次，将划分后的2个局部分组以及全局场景组群，分别利用图卷积网络（Graph Convolutional Network, GCN）进行组交互关系推理,并确定各自组内的关键人物；然后，以全局关系特征作为真实值，将二分组的局部关系特征合并作为预测值，构建两者之间的交叉熵损失函数反馈优化上一级分组交互关系GCN网络，旨在确保2个分组的关键人物与全局关键人物匹配成功。再以全局交互关系中的关键人物信息为指导，分别与2个分组的关键人物进行匹配，将匹配成功后2个小组中的关键人物作为目标节点，建立组间关系图，并经GCN推理得到组间的关系特征；最后，初始特征分别与组间和全局交互关系特征融合得到2个群组行为支路，经过决策融合得到最终的识别结果。实验表明，在Volleyball数据集和NBA数据集上分别取得93.1%和48.1%的准确率。     

全局优化视角下的有色冶金过程建模与控制

为提高生产效率、降低能源消耗、减少环境污染,需要对有色冶金过程进行建模,在系统模型的基础上,通过控制与优化技术,使过程系统运行在最优工况.本文以几个典型有色冶金过程为背景,阐述有色冶金过程建模、控制与优化三者之间的内在关联;从科学研究层次的角度上指出建模、控制与优化分属于不同层次的问题,且从方法论的角度指出建模、控制分两步进行:选择模型结构和估计模型参数、选择控制器结构和整定控制器参数,有色冶金过程系统模型、控制器的结构和参数确定问题均可以看成是非凸优化问题;探讨了全局优化视角下,建模、控制问题转化为优化问题以及在求解优化问题过程中存在的难点,提出解决这些难点的一些可行方案.     

Smart shopper: an agent-based web-mining approach to Internet shopping

This paper presents an agent-based Web-mining approach to Internet shopping. We propose a fuzzy neural network to tackle the uncertainties in practical shopping activities, such as consumer preferences, product specification, product selection, price negotiation, purchase, delivery, after-sales service and evaluation. The fuzzy neural network provides an automatic and autonomous product classification and selection scheme to support fuzzy decision making by integrating fuzzy logic technology and the backpropagation feed forward neural network. In addition, a new visual data model is introduced to overcome the limitations of the current Web browsers that lack flexibility for customers to view products from different perspectives. Such a model also extends the conventional data warehouse schema to deal with intensive data volumes and complex transformations with a high degree of flexibility for multiperspective visualization and morphing capability in an interactive environment. Furthermore, an agent development tool named "Aglet" is used as a programming framework for system implementation. The integration of dynamic object visualization, interactive user interface and data mining decision support provides an effective technique to close the gap between the "real world" and the "cyber world" from a business perspective. The experimental results demonstrate the feasibility of the proposed approach for Web-based business transactions.     

A comparison of classification accuracy of four genetic programming-evolved intelligent structures

We investigate the effectiveness of GP-generated intelligent structures in classification tasks. Specifically, we present and use four context-free grammars to describe (1) decision trees, (2) fuzzy rule-based systems, (3) feedforward neural networks and (4) fuzzy Petri-nets with genetic programming. We apply cellular encoding in order to express feedforward neural networks and fuzzy Petri-nets with arbitrary size and topology. The models then are examined thoroughly in six well-known real world data sets. Results are presented in detail and the competitive advantages and drawbacks of the selected methodologies are discussed, in respect to the nature of each application domain. Conclusions are drawn on the effectiveness and efficiency of the presented approach.     

Recognition of handwritten numerals by Quantum Neural Network with fuzzy features

This paper describes a new kind of neural network – Quantum Neural Network (QNN) – and its application to the recognition of handwritten numerals. QNN combines the advantages of neural modelling and fuzzy theoretic principles. Novel experiments have been designed for in-depth studies of applying the QNN to both real data and confusing images synthesized by morphing. Tests on synthesized data examine QNN's fuzzy decision boundary with the intention to illustrate its mechanism and characteristics, while studies on real data prove its great potential as a handwritten numeral classifier and the special role it plays in multi-expert systems. An effective decision-fusion system is proposed and a high reliability of 99.10% has been achieved. Received October 26, 1998 / Revised January 9, 1999     

基于神经网络的模糊决策树改进算法

传统决策树通过对特征空间的递归划分寻找决策边界,给出特征空间的“硬”划分。但对于处理大数据和复杂模式问题时,这种精确决策边界降低了决策树的泛化能力。为了让决策树算法获得对不精确知识的自动获取,把模糊理论引进了决策树,并在建树过程中,引入神经网络作为决策树叶节点,提出了一种基于神经网络的模糊决策树改进算法。在神经网络模糊决策树中,分类器学习包含两个阶段：第一阶段采用不确定性降低的启发式算法对大数据进行划分,直到节点划分能力低于真实度阈值[ε]停止模糊决策树的增长;第二阶段对该模糊决策树叶节点利用神经网络做具有泛化能力的分类。实验结果表明,相较于传统的分类学习算法,该算法准确率高,对识别大数据和复杂模式的分类问题能够通过结构自适应确定决策树规模。     

A New Fuzzy Support Vector Machine Based on the Weighted Margin

The ideas from fuzzy neural networks and support vector machine (SVM) are incorporated to make SVM classifiers perform better. The influence of the samples with high uncertainty can be decreased by employing the fuzzy membership to weigh the margin of each training vector. The linear separability, fuzzy margin, optimal hyperplane, generalization and soft fuzzy margin algorithms are discussed. A new optimization problem is obtained and SVM is then completely reformulated into a new fuzzy support vector machine (NFSVM). Moreover, the generation bound of NFSVM can be described. We also introduce the membership function in fuzzy neural networks to do some experiments. The results demonstrate that the proposed NFSVM can produce better results than regular SVM and Fuzzy Kernel Perceptron (FKP) in some real cases.     

模糊神经网络及其在时间序列分析中的应用

给出了一种新型的模糊神经网络模型.该模型不需要领域专家的知识进行指导,而是通过对样本竞争分类产生模糊规则.每类样本对应于一条模糊规则,每条模糊规则的后件部分为一个对本类样本进行过学习训练的神经网络.文章以模糊神经网络在时间序列分析中的应用为例,通过与传统的时间序列分析方法以及前向神经网络方法的对比,说明了新型模糊神经网络的有效性.     

A hybrid systematic design for multiobjective market problems: a case study in crude oil markets

This paper studies an application of hybrid systematic design in multiobjective market problems. The target problem is suggested as unstructured real world problem such that the objectives cannot be expressed mathematically and only a set of historical data is utilized.Obviously, traditional methods and even meta-heuristic methods are broken in such cases. Instead, a systematic design using the hybrid of intelligent systems, particularly fuzzy rule base and neural networks can guide the decision maker towards noninferior solutions. The system does not stay in search phase. It also supports the decision maker in selection phase (after the search) to analyze various noninferior points and select the best ones based on the desired goal levels. In addition, numerical examples of real crude oil markets are provided to clarify the accuracy and performance of the developed system.     

Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease

In this study, fuzzy clustering complex-valued neural network (FCCVNN) was proposed to classify portal vein Doppler signals recorded from 54 patients with cirrhosis and 36 healthy subjects. This proposed neural network is a new model for biomedical pattern classification. The FCCVNN was composed of three phases: fuzzy clustering, calculation of FFT values and complex-valued neural network (CVNN). In first phase, fuzzy clustering was done to reduce the number of segments in training pattern. After that, FFT values of Doppler signals were calculated for pre-processing and then obtained values, which include real and imaginary components, were used as the inputs of the CVNN for classification of Doppler signals. Classification results of FCCVNN were evaluated by the different performance evaluation criterion in literature. It shows that Doppler signals were classified successfully with 100% correct classification rate using the proposed method. Moreover, the rates of sensitivity and specificity were calculated as 100% using FCCVNN method. These results were seen to be appropriate with the expected results that are derived from physician’s direct diagnosis. This method would be assisted the physician to make the final decision.     

Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks

In this paper, a novel approach to adjusting the weightings of fuzzy neural networks using a Real-coded Chaotic Quantum-inspired genetic Algorithm (RCQGA) is proposed. Fuzzy neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learning process. To overcome the problems encountered by the conventional learning methods, RCQGA algorithms are adopted because of their capabilities of directed random search for global optimization. It is well known, however, that the searching speed of the conventional quantum genetic algorithms (QGA) is not satisfactory. In this paper, a real-coded chaotic quantum-inspired genetic algorithm (RCQGA) is proposed based on the chaotic and coherent characters of Q-bits. In this algorithm, real chromosomes are inversely mapped to Q-bits in the solution space. Q-bits probability-guided real cross and chaos mutation are applied to the evolution and searching of real chromosomes. Chromosomes consisting of the weightings of the fuzzy neural network are coded as an adjustable vector with real number components that are searched by the RCQGA. Simulation results have shown that faster convergence of the evolution process in searching for an optimal fuzzy neural network can be achieved. Examples of nonlinear functions approximated by using the fuzzy neural network via the RCQGA are demonstrated to illustrate the effectiveness of the proposed method.