基于改进人工势场法的智能无人车路径规划仿真研究

传统的人工势场法由于存在局部极小值问题,使智能无人车无法到达目标点。本文提出一种角度偏移的改进人工势场方法来进行避障的路径规划。介绍传统人工势场模型,详细介绍改进人工势场方法,并且对改进人工势场法进行仿真,实验证明方法的有效性。     

基于差分蜂群算法的电力系统经济负荷分配

针对电力系统经济负荷分配问题,提出一种有效的差分蜂群算法．受差分进化算法的启发,该算法基于差分进化操作改进了雇佣蜂的搜索方式,提高了探索能力和收敛速度．此外,提出一种有效的修复机制以保证新个体的可行性．该算法在带有阀点效应和多燃料特征的典型电力系统经济负荷分配问题上进行了测试．仿真结果验证了所提算法的有效性．     

改进的人工鱼群算法及其在无线定位中的应用*

摘 要：人工鱼群算法是一种基于动物行为的群体智能优化算法。针对基本鱼群算法收敛速度慢和在算法运行后期搜索盲目性较大等问题,给出了一种改进的鱼群算法。该算法采用可变视野,在搜索的后期使用随机步长,并给出了局部最优人工鱼移动策略,较好地平衡了全局搜索能力和局部搜索能力;在假设目标函数分布未知的情况下,规范了人工鱼的各种行为,给出了明确的收敛条件。在文章最后,结合蜂窝网中以功率值作为测量参数的无线定位问题,给出一种基于人工鱼群算法的分布式定位方法。仿真实验表明,该方法实现简单,全局搜索能力强,满足了定位的有效性和实时性。     

一种IP网络友好的Overlay路由机制*

现有IP网络存在不支持QoS,故障恢复速度慢等问题。作为克服或屏蔽这些缺陷的一种有效手段,Overlay网络在当前Internet上被广泛部署和应用。目前存在很多的Overlay路由结构和方法。然而在现存的多层路由中,上层Overlay网络在路由决策时通常对底层的IP网络性能和状态缺乏有效的协调和感知,致使上层Overlay路由和底层IP路由常常处于非协同、非优化工作状态,产生大量路由抖动和次优路由。为解决这些问题,提出了一种底层IP网络友好的Overlay路由机制,其基本思路为：增加Overlay网络的层感知能力,减少路由抖动增加Overlay路由的稳定性。Overlay网络根据底层IP网络的节点跳数、链路带宽、丢包率等反映底层IP网络性能的信息做出路由决策。实际网络中,通过调整Overlay网络的链路代价有效控制Overlay网络的路由决策。最后仿真结果表明：与传统双重路由（Dual Routing）相比,此IP网络友好Overlay路由机制在减少Overlay网络路由抖动,降低Overlay网络路径代价膨胀,提高Overlay网络故障恢复成功率和满足Overlay业务需求等方面有优势。     

基于模式化知识和人工神经网络的智能设计系统框架研究

阐述了将神经网络和模式识别引入智能设计系统的模型－集成转换模型,在该模型中,对智能系统与设计的实现系统的结合进行了探讨,提出了数值编译器与反编译器、图形／编码转换器等将神经网络引入设计系统来的工具和方法。     

A Constraint-Based Approach for Deriving 3-D Structures of Cyclic Polypeptides

This paper presents an hybrid algorithm for deriving 3-D structures of cyclic polypeptides. The algorithm combines constraint-based techniques with the most widely used methods for non-cyclic polypeptides. The empirical results demonstrate that the proposed hybrid algorithm outperforms traditional methods especially with respect to running times.     

Distributed artificial intelligence for FMS scheduling, control and design support

The aim of this paper is to point out some of the abilities of Distributed Artificial Intelligence in the domain of scheduling, control and design support of Flexible Manufacturing Systems. A distributed management system is proposed, based on Distributed Problem Solving, sub-field of Distributed Artificial Intelligence. The basic concepts are the concept of Resource Management Entity to ensure local optimization of the management of resources and the concept of cooperation to provide ability for global and local consistency. The management of resources is associated to activities such as scheduling, control or simulation. It is shown that this system computes not only practicable schedulings, but also presents, on the one hand, some abilities in supporting the design and the robust optimization of Flexible Manufacturing Systems, and, on the other hand, some abilities in supporting real-time control of such systems. This enables, in future works, to design a Distributed Decision Support System for integrated scheduling, control and design support of production systems.     

平板玻璃实时智能自动化系统的应用

目前国内有关智能自动化技术的应用比较少。因此 ,介绍平板玻璃智能自动化系统的原理 ,并结合浮法玻璃的生产工艺进一步介绍了此系统在玻璃生产过程的具体应用 ,旨在为智能自动化技术在玻璃生产中推广应用而抛砖引玉     

Feedforward networks in financial predictions: the future that modifies the present

The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the 'self-momentum', 'Freud' and 'Jung' rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated 'learning to learn' characteristics are now under study and test.     

Artificial Intelligence and Environmental Decision Support Systems

An effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. Problems arise when the quantities of available information are huge and nonuniform (i.e., coming from many different disciplines or sources) and their quality could not be stated in advance. Another associated issue is the dynamical nature of the problem. Computers are central in contemporary environmental protection in tasks such as monitoring, data analysis, communication, information storage and retrieval, so it has been natural to try to integrate and enhance all these tasks with Artificial Intelligence knowledge-based techniques. This paper presents an overview of the impact of Artificial Intelligence techniques on the definition and development of Environmental Decision Support Systems (EDSS) during the last fifteen years. The review highlights the desirable features that an EDSS must show. The paper concludes with a selection of successful applications to a wide range of environmental problems.