共查询到19条相似文献,搜索用时 156 毫秒
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针对人工鱼群算法(AFSA)存在收敛速度慢和寻优精度低等问题,本文提出了一种改进人工鱼群算法(IAFSA).该算法中的人工鱼能够根据鱼群当前状态调整自身的视野和步长来平衡局部搜索和全局搜索.此外,算法中还加入了引导行为,即人工鱼在觅食行为未发现更优的位置时,当前人工鱼向最优人工鱼移动一步.仿真结果表明,改进人工鱼群算法在收敛速度、寻优精度和克服局部极值等方面有很大优势.本文将改进鱼群算法应用时滞系统的辨识中,辨识结果表明改进算法能获取被控对象的精准数学模型,并具有较强的抗干扰能力. 相似文献
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将鱼群算法应用于求解多维背包问题,提出一种求解多维背包问题的鱼群算法.MKPAFSA.定义MKPAFSA 中的各元素,且引入启发因子和动态因子,并对鱼群算法进行了改进和优化.它减少了人工鱼的搜索时间,有效改善了鱼群算法后期收敛较慢且一般仅能得到满意解域的缺陷.仿真试验取得了较好的结果. 相似文献
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针对一类具有凸多面体不确定常参数的离散时间时滞系统,研究其H∞最优保性能预见控制器的设计方法.首先,与以往不同,本文的扩大误差系统仍然保留了时滞,以保证扩大误差系统的状态向量维数不随时滞的增加而增加.其次,针对所构造的扩大误差系统,设计有记忆的状态反馈控制器,并利用线性矩阵不等式方法,导出确保所求控制器存在的条件及该控制器设计的方法.最后,通过建立并求解一个含线性矩阵不等式约束的凸优化问题,给出扩大误差系统的鲁棒H∞保性能控制器,该控制器对于原系统来说就是鲁棒H∞保性能预见控制器. 相似文献
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应用一种全局搜索方法即人工鱼群算法(artificial fish swarm algorithm,AFSA)来优化支持向量基(support vector machines,SVM)的参数,并应用于图像分类.基于分类,初始化惩罚系数C和核函数参数δ2的范围;利用AFSA来优化SVM的参数,并得到合适的值;最后,把参数优化后的SVM应用于分类.实验结果表明,与C-SVC和交叉验证法相比,其分类结果优于其它两种方法,因此AFSA-SVM方法有更好的准确性和鲁棒性. 相似文献
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针对系统存在不确定性扰动时传统UKF滤波算法的滤波精度和鲁棒性均下降的问题,提出了一种基于H∞范数的鲁棒UKF滤波算法.该算法在Krein空间内对简化UKF滤波算法进行改进,增加了一个鲁棒环节.鲁棒环节通过引入给定正常数调整滤波增益从而提高滤波算法的鲁棒性能.在SINS大方位失准角初始对准中对简化UKF滤波算法和鲁棒UKF滤波算法进行了对比研究.仿真结果表明:与简化UKF滤波算法相比,鲁棒UKF滤波算法的方位失准角估计误差由16.9'缩小到4.3'.鲁棒UKF滤波算法降低了系统对扰动的敏感度,具有更好的滤波性能. 相似文献
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针对经典人工蜂群算法收敛速率较慢,后期易陷入局部最优解的不足,本文将粒子群算法中"全局最优"的思想引入到人工蜂群算法的改进过程,从而形成了一种新的人工蜂群改进算法——粒子蜂群算法.首先,提出了趋优度的概念,用来衡量引领蜂在有限次迭代过程中向全局最优解靠近或远离的程度,趋优度值可以评价个体的"发展潜力",趋优度值越低的个体,越需要增大变异的程度,以便找到质量更优的解.其次,专门设计了一种新的蜜蜂群体——粒子蜂,在引领蜂变异阶段根据趋优度的大小将引领蜂变异为侦查蜂和粒子蜂,粒子蜂的出现在很大程度上增加了种群的多样性,拓展了算法的搜索范围.然后,通过粒子蜂群算法种群序列是一个有限齐次马尔科夫链和种群进化单调性的分析,验证了本文所提算法的种群序列依概率1收敛于全局最优解集.最后,将本文所提算法应用于多个常见测试函数,并与经典蜂群算法、近年其他文献改进蜂群算法进行了仿真对比研究,仿真结果表明本文所提算法确实加大了种群的分散度、扩宽了搜索范围,从而具有更快的收敛速度和更高的寻优精度. 相似文献
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针对一类时滞是时变的且属于一个已知区间的线性系统,提出了一种新的Lyapunov-Krasovskii函数分析方法,分析区间时滞线性系统的稳定性,并设计了非脆弱鲁棒H∞控制器.设计的非脆弱鲁棒H∞控制器不仅可以保证时滞系统是渐近稳定的,同时可以满足所给定的H∞性能指标.非脆弱鲁棒H∞控制器可以通过线性矩阵不等式方法获得.仿真例子验证了该方法的有效性. 相似文献
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为确定某银矿的最佳采场结构参数,使该矿开采方案的安全性及经济性最优,构建了Vague-RSM-AFSA模型对采场结构参数进行优化。采用中心复合试验法设计了15个采场结构参数方案,并对各方案进行了数值模拟计算,选取了采场顶板最大沉降位移、间柱最大水平位移、采切比和矿石损失率作为评价指标。基于Vague理论计算了各指标权重及中心复合试验各方案的优越度,采用响应面法(RSM)建立了中心复合试验各方案采场结构参数与优越度的响应面模型,运用人工鱼群算法(AFSA)对优越度响应面模型寻优,得到最佳采场结构参数:采场高度为20 m,采场长度为32.8 m,间柱宽度为16.1 m,方案优越度为0.2631,高于中心复合试验中的最大优越度(0.2271),寻优结果与数值模拟验证结果误差为0.0037,表明Vague-RSM-AFSA模型具有良好的寻优能力及较高的准确性。 相似文献
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This paper investigates the problem of robust guaranteed cost output tracking control for the homing phase of spacecraft rendezvous. Based on the Clohessy-Wiltshire (C-W) equations, and by simultaneously considering the practical situations such as parameter uncertainty, output tracking, performance cost and poles assignment, a new relative dynamic model is developed, and a robust guaranteed cost output tracking control problem is formulated. Then, by a Lyapunov approach, the existence conditions for admissible controllers are formulated in the form of linear matrix inequalities (LMIs), and the controller design is cast into a convex optimization problem subject to LMI constraints. With the obtained controllers, the homing phase can be completed with a guaranteed cost. An illustrative example is provided to show the effectiveness of the proposed controller design method. 相似文献
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为提高无法准确建立数学模型的非线性约束单目标系统优化问题的寻优精度,并考虑获取样本的代价,提出一种基于支持向量机和免疫粒子群算法的组合方法(support vector machine and immune particle swarm optimization,SVM-IPSO).首先,运用支持向量机构建非线性约束单目标系统预测模型,然后,采用引入了免疫系统自我调节机制的免疫粒子群算法在预测模型的基础上对系统寻优.与基于BP神经网络和粒子群算法的组合方法(BP and particle swarm optimization,BP-PSO)进行仿真实验对比,同时,通过减少训练样本,研究了在训练样本较少情况下两种方法的寻优效果.实验结果表明,在相同样本数量条件下,SVM-IPSO方法具有更高的优化能力,并且当样本数量减少时,相比BP-PSO方法,SVM-IPSO方法仍能获得更稳定且更准确的系统寻优值.因此,SVM-IPSO方法为实际中此类问题提供了一个新的更优的解决途径. 相似文献
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针对单核学习支持向量机无法兼顾学习能力与泛化能力以及多核函数参数寻优问题,提出了一种基于群体智能优化的多核学习支持向量机算法。首先,研究了五种单核函数对支持向量机分类性能的影响,进一步提出具有全局性质的多项式核和局部性质的拉普拉斯核凸组合形式的多核学习支持向量机算法;其次,为增加粒子多样性及快速寻优,将粒子群优化算法引入了遗传算法中的杂交操作,并用此改进的群体智能优化算法对多核学习支持向量机进行参数寻优。最后,分别采用深度特征与手工特征作为识别算法的输入,研究表明采用深度特征优于手工特征。故本文采用深度特征作为多核学习支持向量机的输入,以交叉遗传与粒子群混合智能优化算法作为其寻优方式。实验选取合作医院数据集对所提算法进行训练并初步测试,进一步为了验证所提算法的泛化能力,选取公开数据集LUNA16进行测试。实验结果表明,本文算法易于跳出局部最优解,提升了算法的学习能力与泛化能力,具有较优的分类性能。 相似文献
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LIU Yuan-ning DONG Hao ZHANG Hao WANG Gang LI Zhi CHEN Hui-ling 《Canadian Metallurgical Quarterly》2011,27(1)
A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization(PSO).PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space.We designed an efficient objective function according to the minimum free energy,the number of selected stems and the average length of selected stems.We calculated how many legal stems there were in the sequence,and selected some of them to obtain an optimal result using PSO in the right of the objective function.A method based on the improved particle swarm optimization(IPSO)was proposed to predict RNA secondary structure,which consisted of three stages.The first stage was applied to encoding the source sequences,and to exploring all the legal stems.Then,a set of encoded stems were created in order to prepare input data for the second stage.In the second stage,IPSO was responsible for structure selection.At last,the optimal result was obtained from the secondary structures selected via IPSO.Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method.Compared with other six methods,the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results. 相似文献
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This paper proposes a methodology for the optimal design of water distribution systems based on genetic algorithms. The objective of the optimization is to minimize the capital cost, subject to ensuring adequate pressures at all nodes during peak demands. The proposed method is novel in that it involves the use of a pipe index vector to control the genetic algorithm search. The pipe index vector is a measure of the relative importance of pipes in a network in terms of their impact on the hydraulic performance of the network. By using the pipe index vector it is possible to exclude regions of the search space where impractical and infeasible solutions exist. By reducing the search space it is possible to generate feasible solutions more quickly and hence process much healthier populations than would be the case in a standard genetic algorithm. This results in optimal solutions being found in a fewer number of generations resulting in a substantial saving in terms of computational time. The method has been tested on several networks, including networks used for benchmark testing least cost design algorithms, and has been shown to be efficient and robust. 相似文献
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The writers have come across a difficult stability analysis problem where there are several “strong” local minima. Several well known heuristic global minimum methods fail to locate the global minimum for this case, and the writers finally adopt the artificial fish swarms algorithm to overcome this difficult problem. This optimization algorithm is demonstrated to be effective and efficient for normal problems. To illustrate the effectiveness of the proposed algorithm, three difficult examples are considered. The sensitivity of the proposed algorithm with respect to the parameters used for the global optimization algorithm will also be investigated in this paper. 相似文献