基于虚拟力的群机器人队形控制

提出了一种基于虚拟力的群机器人队形形成原理,提出了一种使用拟合法构建虚拟力模型的方法及其应用。实验结果表明该方法可以快速、有效地完成队形分布,并指出了今后的研究方向。     

基于Petri网的工作流时间动态预测及验证

目前,工作流管理系统不能有效的处理时间管理问题,为了动态预测活动发生的时间间隔,验证时间的一致性,预知潜在的时间冲突,首先建立基于时间Petri网的扩展工作流网(XTWF-net),然后根据工作流管理联盟规定的几种基本结构推导出时间预测规则,并采用面向对象技术实现了预测算法,最后给出了动态验证的方法和决策策略.     

ACS algorithm-based adaptive fuzzy PID controller and its application to CIP-Ⅰ intelligent leg

Based on the ant colony system(ACS)algorithm and fuzzy logic control,a new design method for optimal fuzzy PID controller was proposed.In this method,the ACS algorithm was used to optimize the input/output scaling factors of fuzzy PID controller to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object.The designed controller,called the Fuzzy-ACS PID controller,was used to control the CIP-I intelligent leg.The simulation experiments demonstrate that this controller has good control performance.Compared with other three optimal PID controllers designed respectively by using the differential evolution algorithm,the real-coded genetic algorithm,and the simulated annealing,it was verified that the Fuzzy-ACS PID controller has better control performance.Furthermore,the simulation results also verify that the proposed ACS algorithm has quick convergence speed,small solution variation,good dynamic convergence behavior,and high computation efficiency in searching for the optimal input/output scaling factors.     

A composite particle swarm algorithm for global optimization of multimodal functions

During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.     

基于频率筛分的无监督人体姿态特征提取与识别研究

基于视频序列的人体行为分析需要检测和判别人体姿态,已有人体姿态检测与判别方法往往达不到实用性要求.从两个方面探讨应用BEMD(bidimensional empirical mode decomposition)算法提升特征分离度与判别性,以进行人体姿态检测和判别:BEMD分解源图像得到的多层固有模态图BIMF具有判别特征,可形成具有强边缘的对比度高的区域,其中包括人体轮廓区域;从低分辨率尺度BIMF图像到高分辨率尺度BIMF图像递归计算,建立基于BEMD的多尺度树(BEMD muhiscale-trees tructured)模型,快速提取目标区域并获取人体形状轮廓特征.实验证明,利用该方法进行人体姿态轮廓特征提取,并建立人体姿态的简化模型,可快速检测并判别人体姿态,以达到实时识别.     

一种基于AIS和Bayes网络的垃圾邮件过滤算法

将人工免疫系统和贝叶斯网络相结合应用于垃圾邮件的过滤，设计了一个基于AIS和Bayes网络的垃圾邮件过滤算法，分析并解决了该算法实现过程中的关键问题，给出了亲和力的计算方法和抗原/抗体的结构定义。在PU1bare 语料的基础上与Carreras提出的AdaBoost方法进行对比实验，最后给出了实验结果。     

多机器人系统的研究现状

介绍了多机器人系统的研究进展及现状,对该领域内的主要内容(思想源泉、体系结构与控制、通信、学习、可重构机器人)的研究现状作了分析和介绍,还提出了一些未来值得研究的议题。     

基于改进的人工免疫算法的函数优化

为了提高算法的运行速度和收敛速度,确保算法收敛到全局最优,以及提高群体的多样性和整体品质,提出了基于百分比表示的抗体相似度、期望繁殖率以及克隆选择概率的定义方法和计算公式,并结合精英策略(elitism strategy)提出了一种改进的人工免疫算法,(Artificial Immune Algorithm with Elitism,AIAE).用AIAE对测试函数F15进行仿真实验,结果表明所提算法能以较快的速度搜索到函数的全局最优解,并且解的波动性很小、解的质量很高.因此可将该算法用来优化由本项目设计组设计的智能人工腿中的控制器.