Description: 量子粒子群算法 mathlab,专门使用量子理论来提高粒子群算法的效率，经实验表明，运算准确性和效率有很大提升-Quantum Particle Swarm Optimization mathlab, specifically the use of quantum theory to improve the efficiency of particle swarm optimization, the experiments show that the accuracy and efficiency of computing has a great upgrade Platform: |
Size: 4096 |
Author:jason |
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Description: 在网络异常检测中,为了提高对异常状态的检测率，降低对正常状态的误判率,本文提出一种基于量子粒子群优化算法训练小波神经网络进行网络异常检测的新方法。利用量子粒子群优化算法(QPSO)训练小波神经网络,将小波神经网络(WNN)中的参数组合作为优化算法中的一个粒子,在全局空间中搜索具有最优适应值的参数向量。- In order to improve the detection rate for anomaly state and reduce the false positive rate for normal state in the network anomaly detection, a novel method of network anomaly detection based on constructing wavelet neural network (WNN) using quantum-behaved particle swarm optimization (QPSO) algorithm was proposed. The WNN was trained by QPSO. Platform: |
Size: 1024 |
Author:liang |
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Description: 基本的粒子群算法以及使用具有量子行为的粒子群算法，能够很好的用于全局的寻优-The basic particle swarm optimization and the use of a quantum particle swarm behavior can be very good for the overall optimization Platform: |
Size: 2048 |
Author:刘兴阳 |
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Description: 探讨了经典的粒子群优化算法，在此基础上，提出了量子粒子群算法（QPSO），该算法具有高效的全局搜索能力-Of the classical particle swarm optimization, on this basis, proposed a quantum particle swarm optimization (QPSO), the algorithm is efficient global search capability Platform: |
Size: 3072 |
Author:陈颖 |
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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 Platform: |
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Author:zhuifenger |
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Description: 在网络异常检测中,为了提高对异常状态的检测率，降低对正常状态的误判率,本文提出一种基于量子粒子群优化算法训练小波神经网络进行网络异常检测的新方法。利用用量子粒子群优化算法(QPSO)训练小波神经网络,将小波神经网络(WNN)中的参数组合作为优化算法中的一个粒子,在全局空间中搜索具有最优适应值的参数向量。
-This paper presents a new method of network anomaly detection based on quantum particle swarm optimization algorithm to train the wavelet neural network in order to improve the rate of detection of the abnormal state and reduce the false positive rate for normal state in the network anomaly detection. Quantum particle swarm optimization algorithm (QPSO) training wavelet neural network, the combination of parameters of the wavelet neural network (WNN) as a particle optimization algorithm search parameter vector with the best fitness value in the global space. Platform: |
Size: 1024 |
Author:zhou |
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Description: 实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。
对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。
深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。
利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。
-SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification. Platform: |
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Author:招伟杰 |
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Description: 程序6：遗传算法工具箱使用例子；
程序7：多种群遗传算法的函数优化；
程序8：基于量子遗传算法的函数寻优；
程序9：基于遗传算法的多目标优化算法；
程序10：基于粒子群算法的多目标搜索算法-Program 6: examples of the use of genetic algorithm toolbox Program 7: multi-population genetic algorithm function optimization Program 8: a function based on quantum genetic algorithm optimization Program 9: Based on the genetic algorithm for multi-objective optimization algorithm Program 10: Based on the particle swarm optimization algorithm for multi-objective search Platform: |
Size: 4096 |
Author:王晓卫 |
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Description: The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Platform: |
Size: 2048 |
Author:ramin |
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Description: 提出了一种基于QPSO(Quantum Particle Swarm Optimization)的神经网络训练算法,并对神经网络进行训练,将其结果与传统神经网络训练算法相比较,该算法具有收敛速度快,对数据离散程度高的问题更易实现全局收敛的特点。-A training algorithm of neural network based on QPSO is ProPosed,whieh train the neural network by means of the quantum Partiele swarm optimization. The
algorithm show better convergent speed and better global convergent charaeteristie when dealing with more dispersed issue compared with the traditional neural network
training algoritm. Platform: |
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Author:吴鸣 |
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Description: 针对单级多资源约束生产批量计划问题,提出了基于量子粒子群算法求解该问题的方法。此算法将量子强大的领域搜索能力和基本粒子群算法(PSO)通过跟踪极值更新粒子的功能-On a single stage production lot-sizing problem of resource constraints, based on quantum particle swarm algorithm is proposed to solve the problem.The algorithm to the field of quantum powerful search ability and basic particle swarm optimization (PSO) through the tracking function of extremum updating particles Platform: |
Size: 2048 |
Author:张亿 |
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Description: 此為應用MATHCAD來編寫BPSO，為粒子群優演算法，可應用於求解最佳化過程，可跑小系統之模擬，簡單又好用-This is the application to write MATHCAD BPSO, particle swarm optimization algorithm can be applied to solve the optimization process, the simulation can run a small system, simple and easy to use Platform: |
Size: 18432 |
Author:張騰文 |
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Description: 一个关于量子粒子群优化算法的matlab程序，有助于学习和理解该算法的原理。-The principle of a quantum particle swarm optimization algorithm matlab program, contribute to learning and understanding of the algorithm Platform: |
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Author:1121261428 |
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Description: 通过改进获得一种量子行为的粒子群优化算法，并将该算法应用于某机组的燃烧控制系统的建模研究，获得不同工况下的传递函数形式的系统模型。-Obtain a quantum behavior improved particle swarm optimization algorithm, and the algorithm is applied to the model building of a unit combustion control system, access to system transfer function of the model under different conditions. Platform: |
Size: 5248000 |
Author:ShiYanggen |
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Description: 本人所写的基于量子行为优化的粒子群算法，很好的解决了粒子群算法易于陷入局部最优解的缺点-I wrote particle swarm optimization based on quantum-behaved, a good solution to the shortcomings of particle swarm algorithm is easy to fall into local optima Platform: |
Size: 2048 |
Author:mikhansay |
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Description: 量子粒子群优化算法以量子力学原理为基础，用波函数描述粒子的运动状 态，通过测量操作引导粒子搜索全局最优解。量子系统的不确定性决定了粒子能够以一定的概率出现在整个可行域内，克服了粒子群优化算法因为粒子速度的限制， 使粒子只能限定在某个局部区域的问题。(Quantum particle swarm optimization (QPSO) algorithm is based on the principle of quantum mechanics. The wave function is used to describe the motion state of particles, and the particles are searched for global optimal solution by measuring operation. The quantum system is decided by the uncertainties of particles with a certain probability in the whole feasible region, to overcome the particle swarm optimization algorithm for particle speed limit, so that the particles can only be limited in a local region of the problem.) Platform: |
Size: 1024 |
Author:舒逸流风
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Search - A Quantum Particle Swarm Optimization