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Description: matlab编的一种峰值搜索算法 很好用
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Size: 15480 |
Author: xidianllf@sina.com |
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Description: 应用于LABWINDOWS下的信号处理算法，主要功能是实现信号峰值的自动搜索-LABWINDOWS under the applied signal processing algorithms, the main function is to automatically search for signal peaks
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Size: 7168 |
Author: www |
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Description: 利用music算法进行谱峰搜索，对一模拟信号的空间谱的峰值进行搜索-spectral-peak-searching music matlab
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Size: 1024 |
Author: vinghong |
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Description: 针对多峰函数优化问题，借鉴混沌遍历特性和免疫网络理论，提出一种免疫混沌网络算法。算法利用混沌运动的自身规律在不同的峰值区域内搜索最佳抗体，增强了算法的局部搜索能力；采用网络抑制策略，保持了种群的多样性；通过网络补充机制自适应地调节抗体群的规模，提高了算法对不同类型多峰函数的适应能力。仿真结果表明该算法能有效地改善种群的多样性，较好地保持全局搜索和局部搜索的动态平衡，具有更强的多峰函数优化能力-Referred to the ergodicity of chaos and immune network theory, an immune chaotic network algorithm for
multimodal function optimization was proposed. The rule of chaotic motion was used to search the best antibodies in different peak regions in order to enhance the capacity of local search. The strategy of network suppression was adopted to maintain the diversity of population. Under the action of network supplement mechanism, the scale of antibody population was adjusted to adapt different types of multimodal function. Simulation results show that the algorithm can not only improve population diversity effectively, but also keep the dynamic balance between global search and local search well. Therefore, it has excellent optimization performance to multimodal function.
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Size: 1095680 |
Author: 崔冰 |
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Description: 峰值搜索的算法,可对法来波方向进行估计中的谱峰提取的一种方法-Peak search algorithm, the direction of the method to estimate the wave spectrum peak extraction method
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Size: 1024 |
Author: Joey |
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Description: 线搜索求简单单峰值函数的最小值程序设计，算法采用固定搜索方向方法。-Simple single-peak line search function to find the minimum programming
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Size: 1024 |
Author: fsc |
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Description: 工程应用中的多峰寻优问题要求搜索目标函数的多个极值点，现有的多峰优化方法难以直接利用应用
问题的先验知识引导算法过程，多峰寻优效率较低。基于粒子群优化算法设计一种面向应用的多峰寻优算法，
能有效利用易于获得的先验参数，如峰间分辨率、峰位置精度、峰值个数等实现快速多峰搜索。该算法保持了粒
子群算法的简单性并改善了搜索多样性，使其可控地收敛到多个峰值上。将该算法与几种典型的多峰寻优方法
进行了对比测试和分析，结果表明，对复杂多峰函数，该算法能以最快的收敛速度实现多峰搜索-Multimodal optimization problem in engineering applications require more extreme points of the search objective function the existing multimodal optimization method is difficult to directly use the application of a priori knowledge to guide the algorithm process less efficient multimodal optimization. Based on particle swarm optimization algorithm to design an application-oriented multimodal optimization algorithm can effectively take advantage of the easy access to the a priori parameters, such as the resolution of the peak-to-peak, peak position accuracy, the number of peaks to achieve fast multimodal search. The algorithm keeps controllable particle swarm algorithm simple and to improve the search for diversity, it converges to multiple peaks. This algorithm with several typical multimodal optimization method comparison test and analysis results show that the complex multimodal function, the algorithm can achieve the fastest convergence rate multimodal search
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Size: 386048 |
Author: zhuifenger |
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Description: 介绍了一种利用量子行为粒子群算法(QPSO)求解多峰函数优化问题的方法。为此，在
QPSO中引进一种物种形成策略，该方法根据群体微粒的相似度并行地分成子群体。每个子群体是
围绕一个群体种子而建立的。对每个子群体通过QPSO算法进行最优搜索。从而保证每个峰值都有
同等机会被找到，因此该方法具有良好的局部寻优特性。将基于物种形成的QPSO算法与粒子群算
法(PSO)对多峰优化问题的结果进行比较。对几个重要的测试函数进行仿真实验结果证明，基于物
种形成的QPSO算法可以尽可能多地找到峰值点，峰值收敛性能优于PS-A quantum behaved particle swarm optimization (QPSO) method for solving multimodal function optimization problems. For this reason, the introduction of a species in QPSO form a strategy, the method is based on the similarity of the groups of particles divided into sub-groups in parallel. Each sub-group is established around the seeds of a group. The QPSO algorithm optimal search for each sub-group. In order to ensure that each peak has the same opportunity to find, this method has good local optimization features. Will compare the results of multimodal optimization problems based on QPSO algorithm and particle swarm optimization (PSO) species formed. Simulation results prove that several important test function, based species formed QPSO algorithm can be as much as possible to find the peak point, the peak convergence outperforms PS
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Size: 343040 |
Author: zhuifenger |
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Description: 针对小生境粒子群优化技术中小生境半径等参数选取问题，提出了一种新颖的小生境方法，无须小生
境半径等任何参数。通过监视粒子正切函数值的变化，判断各个粒子是否属于同一座山峰，使其追踪所在山峰
的最优粒子飞行，进而搜索到每一座山峰极值。算法实现简单，不仅克服了小生境使用中需要参数的弊端，而且
解决了粒子群算法只能找到一个解的不足。最后通过对多峰值函数的仿真实验，验证了算法可以准确地找到所
有山峰-Proposed a novel niche for niche particle swarm optimization technology niche radius parameter selection problem, without niche habitat radius of any parameter. By monitoring changes in the value of the particle tangent function to determine whether the individual particles belong with a mountain to make it the optimal particle track where the mountain flight, and then searched every hill extremum. The algorithm is simple, not only to overcome the drawbacks of the need to use the niche parameters, and solve the particle swarm algorithm can only find a solution to the lack of. Finally, through the simulation of multi-peak function to verify the algorithm can accurately find all the peaks
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Size: 379904 |
Author: zhuifenger |
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Description: 针对小生境粒子群优化技术中小生境半径等参数选取问题，提出了一种新颖的小生境方法，无须小生
境半径等任何参数。通过监视粒子正切函数值的变化，判断各个粒子是否属于同一座山峰，使其追踪所在山峰
的最优粒子飞行，进而搜索到每一座山峰极值。算法实现简单，不仅克服了小生境使用中需要参数的弊端，而且
解决了粒子群算法只能找到一个解的不足。最后通过对多峰值函数的仿真实验，验证了算法可以准确地找到所
有山峰-Proposed a novel niche for niche particle swarm optimization technology niche radius parameter selection problem, without niche habitat radius of any parameter. By monitoring changes in the value of the particle tangent function to determine whether the individual particles belong with a mountain to make it the optimal particle track where the mountain flight, and then searched every hill extremum. The algorithm is simple, not only to overcome the drawbacks of the need to use the niche parameters, and solve the particle swarm algorithm can only find a solution to the lack of. Finally, through the simulation of multi-peak function to verify the algorithm can accurately find all the peaks
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Size: 434176 |
Author: zhuifenger |
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Description: 遗传算法虽然全局搜索能力较强，但是局部搜索能力较弱，一般只能搜索到函数优化问题的次优解，而不是最优解，特别是函数具有多个峰值时，遗传算法易陷入局部极小，不能找到真正的全局最优解。非线性规划因多采用梯度下降方法求解，而具有极强的局部搜索能力。因此，本源代码结合两种算法的优点，一方面采用遗传算法进行全局搜索，另一方面采用非线性规划进行局部搜索，以得到函数优化问题的全局最优解。实验证明，这种方法不仅能解决多峰函数寻优易陷入局部极小的问题，而且具有很高的迭代寻优效率，取得了满意的结果。-Global search ability of genetic algorithms, local search capability is weak, generally only be able to search a sub-optimal solution to the function optimization problem, rather than the optimal solution, especially the function has multiple peaks, genetic algorithm is easy to fall into the local polar can not find the true global optimal solution. Solving nonlinear programming due to the use of the gradient descent method, and has strong local search ability. Source code algorithm combines two advantages, on the one hand, the use of genetic algorithms for global search, on the other hand by nonlinear programming local search to obtain the global optimum function optimization problems. Experiments show that this method can not only solve the multimodal function optimization easy to fall into local minima problems, and has a highly iterative optimization efficiency, and achieved satisfactory results.
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Size: 44032 |
Author: 乐乐 |
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Description: 基于快速搜索数据密度峰值的聚类算法是一种基于聚类中心具有较近邻点有更高密度且其与更高密度点间有着较大的相对距离的一类算法。-Clustering by fast search and find of density peaks is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance points with higher densities.
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Size: 6172672 |
Author: 王志勇 |
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Description: 基于三维曲面的顶点搜索算法，可以在一个三维曲面中很快的找到峰值。-Based on three-dimensional surface vertex search algorithm can quickly find a peak in a three-dimensional surface.
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Size: 2048 |
Author: 王凯 |
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Description: 免疫算法来实现TSP问题的求解免疫算法正是这一思路的产物，是受生物免疫系统的启示而设计出来的一种具有对多峰值函数进行多峰值搜索及全局寻优能力的新型算法。-Using immune algorithm to achieve the TSP problem solving, program-wide, you can run.
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Size: 268288 |
Author: zhch96 |
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Description: 自动选择聚类中心的快速搜索密度峰值聚类算法(A fast search algorithm for density peak clustering based on automatic selection of clustering centers)
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Size: 6173696 |
Author: jhgsjfv
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Description: 基于FrFT的时变幅度LFM信号参数估计，采用二维峰值搜索算法，后用一维曲线拟合，大大减少了计算量。(The parameter estimation of LFM signal based on FrFT is based on two dimensional peak search algorithm and one dimension curve fitting, which greatly reduces the computation.)
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Size: 7168 |
Author: 张翠翠 |
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