基于多Agent技术的分布式协同设计结构的研究

将多Agent技术引入协同设计中，使所构造的协同设计系统具有多Agent系统的分布性、协作性、智能性的特点。文中给出了Agent的结构、协同设计单元的多Agent组成和基于Web服务的整个协同设计系统的结构； 并对协同设计系统实现的一些关键技术进行了讨论，包括Agent的创建、Agent之间的交互、知识的共享和Agent之间的冲突的消解。     

网络结构化多Agent系统的任务分配

网络结构化多Agent系统既包括系统运行的底层物理网络,还包括Agent之间的交互网络。传统的任务分配方式并没有深入考虑到网络结构化的特点。文中首先论述网络结构化多Agent系统中任务分配的特点,介绍和分析基于底层网络拓扑与资源分布的任务分配方式、基于Agent交互网络与资源分布的任务分配方式和基于综合网络情境资源的任务分配方式。然后对相关工作进行综述,并与网络结构化多Agent系统任务分配模型进行比较分析。最后论述该方向的难点和未来要解决的问题。     

基于神经网络的Agent个性化行为选择

在基于效用的行为选择模型基础上对多Agent系统中个性建模问题进行研究。利用人工神经网络能够学习到人类难以理解的目标函数的特点,结合心理学中个性的五因素模型建立Agent个性神经网络,通过不同参数反映个性对效用变化的影响方式,具有更强的个性表征能力。设计梯度下降的学习算法训练Agent相应的个性神经网络。实验验证了该模型刻画Agent个性的有效性。     

城轨列车运行自动监控系统的多Agent模型

城市轨道交通列车自动监控系统ATS(Automatic Train Supervision)国产化具有重要经济效益和社会效益.基于多Agent的模型对ATS系统分析、设计、仿真、优化提供支持.在分析城轨ATS原理的基础上,建立了城轨ATS系统的多Agent模型.给出了ATS多Agent模型的组成结构,模型中最重要的车辆Agent和中央Agent内部结构,分析了模型中各Agent的功能划分,讨论了模型中Agent的通信问题.     

基于Web的多Agent的异地制造

论文提出一种基于Web的多Agent的异地制造系统,分析系统的结构,阐述主要Agent的功能和主要工作原理。基于Web的多Agent的异地制造系统具有结构灵活、适应性强、应用范围广等特点。给出了实现的主要方法,最后将其应用于实际企业,取得满意的效果。     

基于Agent的电器设备智能测控网络的研究

基于LonWonks的测控网络结构,采用面向Agent的设计思想,结合神经网络和模糊控制的技术特点,提出了一种实现网络电器智能控制的Agent模型。     

基于DMDE算法的前向神经网络设计

针对神经网络结构与参数并行优化问题,提出一种基于动态多群体差分进化算法的前向神经网络设计方法。采用分层递阶结构原理构造算法个体,根据控制基因信息将个体分成不同的动态群体。通过对个体进行重构,实现进化过程中个体信息的充分交换与共享。设计基于群体适应度的控制基因更新方法来优化网络拓扑结构,克服结构优化的盲目与低效问题。将所设计的神经网络应用于大包线飞行控制律参数拟合中。仿真结果表明,该算法能快速有效地确定神经网络的结构和权值,所优化的网络在调参控制中具有较好的泛化能力。     

Java类文件解析Agent的设计原理与实现

基于构件的开发是软件重用的主流技术。文章以JavaBean构件为研究对象,对实现基于多Agent的JavaBean构件挖掘系统的核心技术类文件解析Agent(ClassParserAgent)的实现原理、结构及功能作了具体的介绍。由于移动Agent技术具有众多潜在的优点,所以可以将类文件解析Agent定位到未来能够移动到远程服务器上完成相关任务,在类文件解析Agent的移动能力的实现上进行了详细的分析。     

融合免疫原理与多Agent网络安全系统研究

针对免疫系统不能适用于所有安全机制来解决网络安全的问题.通过将免疫系统和多Agent系统的多种特性引入网络安全系统,提出了一种基于免疫原理和多Agent融合方法的网络安全系统模型.设计了具有两种类型Agent的多Agent系统,并以访问控制、IDS、Honeypot等网络安全工具定义了这些Agent,使网络安全系统提高了适应性能力等特征.     

基于CBR的信息系统案例检索多Agent模型研究

将Multi-Agent技术应用于信息系统案例检索中,结合CBR技术与Web Service思想,提出了基于CBR的信息系统案例检索多Agent系统的模型框架和运作流程,设计了基于智能聚类的案例检索算法,通过神经网络的自组织学习优化案例检索的过程,使得该多Agent系统成为具有高度自治性的自我学习与完善的系统,为信息系统案例检索系统的研究与开发提供了一定的借鉴.     

基于生物脑网络连接特性的高效神经网络算法

人工神经网络发展至今,已经在计算机视觉、类脑智能等方面得到广泛应用.在过去几十年中,人们对神经网络的研究注重追求更高的准确率,从而忽略了对网络计算成本的控制.而人脑作为高效且节能的网络,其对人工智能的发展起到了重要启示作用.如何仿真生物脑网络的连接特性,建立超低能耗的人工神经网络模型实现基本相同的目标识别正确率成为当前研究的热点.为建立低能耗的人工神经网络模型,本文结合大脑网络的连接特性,通过改变人工神经网络的连接实现网络的高效性.实验结果表明,结合生物脑网络的连接特性,改变网络的连接,很大程度上减少了网络的计算成本,而网络的性能并没有受到明显影响.     

Representing knowledge by neural networks for qualitative analysisand reasoning

A systematic approach has been developed to construct neural networks for qualitative analysis and reasoning. These neural networks are used as specialized parallel distributed processors for solving constraint satisfaction problems. A typical application of such a neural network is to determine a reasonable change of a system after one or more of its variables are changed. A six-node neural network is developed to represent fundamental qualitative relations. A larger neural network can be constructed hierarchically for a system to be modeled by using six-node neural networks as building blocks. The complexity of the neural network building process is thus kept manageable. An example of developing a neural network reasoning model for a transistor equivalent circuit is demonstrated. The use of this neural network model in the equivalent circuit parameter extraction process is also described     

Ensemble of Evolving Neural Networks in Classification

The ensemble of evolving neural networks, which employs neural networks and genetic algorithms, is developed for classification problems in data mining. This network meets data mining requirements such as smart architecture, user interaction, and performance. The evolving neural network has a smart architecture in that it is able to select inputs from the environment and controls its topology. A built-in objective function of the network offers user interaction for customized classification. The bagging technique, which uses a portion of the training set in multiple networks, is applied to the ensemble of evolving neural networks in order to improve classification performance. The ensemble of evolving neural networks is tested by various data sets and produces better performance than both classical neural networks and simple ensemble methods.     

A reference model approach to stability analysis of neural networks

In this paper, a novel methodology called a reference model approach to stability analysis of neural networks is proposed. The core of the new approach is to study a neural network model with reference to other related models, so that different modeling approaches can be combinatively used and powerfully cross-fertilized. Focused on two representative neural network modeling approaches (the neuron state modeling approach and the local field modeling approach), we establish a rigorous theoretical basis on the feasibility and efficiency of the reference model approach. The new approach has been used to develop a series of new, generic stability theories for various neural network models. These results have been applied to several typical neural network systems including the Hopfield-type neural networks, the recurrent back-propagation neural networks, the BSB-type neural networks, the bound-constraints optimization neural networks, and the cellular neural networks. The results obtained unify, sharpen or generalize most of the existing stability assertions, and illustrate the feasibility and power of the new method.     

利用遗传模拟退火算法优化神经网络结构

常用的神经网络是通过固定的网络结构得到最优权值,使网络的实用性受到影响。引入了一种基于方向的交叉算子和变异算子,同时把模拟退火算法引入了遗传算法,结合遗传算法和模拟退火算法的优点,提出了一种优化神经网络结构的遗传——模拟退火混合算法,实现了网络结构和权值的同时优化。仿真实验表明,与遗传算法和模拟退火算法相比,该算法优化的神经网络收敛速度较快、预测精度较高,提高了网络的处理能力。     

小波混沌神经网络模拟退火参数研究

小波混沌神经网络已经成功地解决了函数优化和组合优化问题。研究了分段指数退火函数的Morlet小波混沌神经元模型,给出了分段小波混沌神经元的倒分岔图和Lyapunov指数图。在小波混沌神经网络的基础上,加入了分段指数退火函数,提出了一种新的改进的小波混沌神经网络,并把它应用到函数优化和组合优化问题中。仿真结果表明,改善了小波混沌神经网络的寻优能力,改进的小波混沌神经网络优于原来的小波混沌神经网络。     

POWER QUALITY DISTURBANCE CLASSIFICATION USING S-TRANSFORM AND RADIAL BASIS NETWORK

This article presents an artificial neural network (ANN)-based approach for power quality (PQ) disturbance classification. The input features of the ANN are extracted using S-transform. The features obtained from the S-transform are distinct, understandable, and immune to noise. These features after normalization are given to radial basis function (RBF) neural networks. The data required to develop the network are generated by simulating various faults in a test system. The proposed method requires a lesser number of features and less memory space without losing its original property. The simulation results show that the proposed method is effective and can classify the disturbance signals even under a noisy environment.     

Primal and dual assignment networks.

This paper presents two recurrent neural networks for solving the assignment problem. Simplifying the architecture of a recurrent neural network based on the primal assignment problem, the first recurrent neural network, called the primal assignment network, has less complex connectivity than its predecessor. The second recurrent neural network, called the dual assignment network, based on the dual assignment problem, is even simpler in architecture than the primal assignment network. The primal and dual assignment networks are guaranteed to make optimal assignment. The applications of the primal and dual assignment networks for sorting and shortest-path routing are discussed. The performance and operating characteristics of the dual assignment network are demonstrated by means of illustrative examples.     

改进的Elman模型与递归反传控制神经网络

在Elman网络的基础上提出了两种改进网络:输出-输入反馈Elman网络和输出-隐层反馈Elman网络模型,并以前者作为误差反传的通道,建立了递归反向传播控制神经网络模型.在Lyapunov稳定性意义下分别给出了改进网络的稳定性证明,得到了保证网络稳定收敛的最佳自适应学习速率.分别用Elman网络及其改进网络对超声马达进行了模拟.利用改进的Elman网络模型,除了可以较好地模拟马达速度以外,还得到了一些有意义的结果,据此可以根据现场数据采样的情况,选用不同的网络模型.模拟实验结果表明,递归反向传播控制神经网络对多种形式的超声马达参考速度都有很好的控制效果.     

Transputers and neural networks: an analysis of implementationconstraints and performance

A performance analysis is presented that focuses on the achievable speedup of a neural network implementation and on the optimal size of a processor network (transputers or multicomputers that communicate in a comparable manner). For fully and randomly connected neural networks the topology of the processor network can only have a small, constant effect on the iteration time. With randomly connected neural networks, even severely limiting node fan-in has only a negligible effect on decreasing the communication overhead. The class of modular neural networks is studied as a separate case which is shown to have better implementation characteristics. On the basis of implementation constraints, it is argued that randomly connected neural networks cannot be realistic models of the brain.