Description: The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.-The EM algorithm is short for Expectation - Maximization algorithm. It is based on an ITERA tive optimization of the centers and widths of t he kernels. The aim is to optimize the likelihoo d that the given data points are generated by a mi xture of Gaussians. The numbers next to the Gaus sians give the relative importance (amplitude ) of each component. Platform: |
Size: 15614 |
Author:陈伟 |
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Description: pso算法的代码，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值，-PSO algorithm code is based on an iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values, Platform: |
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Author:张林 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross - (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications Platform: |
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Author:张正 |
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Description: The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.-The EM algorithm is short for Expectation- Maximization algorithm. It is based on an ITERA tive optimization of the centers and widths of t he kernels. The aim is to optimize the likelihoo d that the given data points are generated by a mi xture of Gaussians. The numbers next to the Gaus sians give the relative importance (amplitude ) of each component. Platform: |
Size: 15360 |
Author:陈伟 |
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Description: pso算法的代码，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值，-PSO algorithm code is based on an iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values, Platform: |
Size: 128000 |
Author:张林 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhar博士和kennedy博士发明。源于对鸟群捕食的行为研究 ，PSO同遗传算法类似，是一种基于叠代的优化工具。
-Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), and has Eberhar Dr. Dr. kennedy invention. Stems from the behavior of predatory birds, PSO with genetic algorithm is similar to an iterative optimization-based tools. Platform: |
Size: 2048 |
Author:叶开 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究
PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍
同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications Platform: |
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Author:zzh |
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Description: 微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。-Particle Swarm Optimization [PSO] are equal by Kennedy and Eberhart in 1995 developed an evolutionary computing technology, from preying on the birds of the simulation process. PSO with genetic algorithm is similar to an iterative optimization-based tool, but the use of genetic algorithms and genetic manipulation of different sub-optimize the use of groups between the various entities within the " collaboration" and " competitive" relationship, according to themselves and their competition the flying experience, adjust their behavior. Comparison with genetic algorithms, PSO has the advantage of being simple and easy and did not realize the need to adjust the parameters much. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications. Platform: |
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Author:wzy |
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Description: 本程序为一种基于凸优化的OFDM信道估计算法，平滑SLO算法是求解稀疏线性方程组的一种迭代解法，本程序将其应用于OFDM信道估计，取得了较好的信号估计精度以及速度-This procedure is a convex optimization of OFDM-based channel estimation algorithm, smoothing algorithm SLO is sparse system of linear equations to solve an iterative method, the procedure will be applied to OFDM channel estimation, the signal to obtain a better estimation accuracy and speed Platform: |
Size: 4096 |
Author:赵亮 |
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Description: PSO同遗传算法类似，是一种基于迭代的优化算法-PSO is similar with the genetic algorithm is an iterative optimization algorithm based on Platform: |
Size: 6144 |
Author:naiyu |
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Description: 粒子群优化算法是一种进化优化技术，源于对鸟群扑食的行为，是一种基于迭代的优化工具。此文件提供了基本粒子群算法、带压缩因子的粒子群算法、二阶粒子群算法、二阶振荡粒子群算法、权重改进的粒子群算法、混沌粒子群算法、基于杂交的粒子群算法、基于模拟退火的粒子群算法的MATLAB源代码。-PSO is an evolutionary optimization technique, derived from the behavior of the birds of prey, is based on iterative optimization tools. This document provides basic particle swarm algorithm, with a compression factor of the particle swarm algorithm, particle swarm optimization order, second order oscillating particle swarm algorithm, the weight particle swarm algorithm, chaotic particle swarm algorithm, based on hybrid particle swarm algorithm, based on Simulated annealing particle swarm optimization MATLAB source code. Platform: |
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Author:熊杰 |
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Description: 基于OFDMA系统的多用户资源分配算法，论文配备MATLAB代码，入门的好教材。-— Orthogonal Frequency Division Multiple Access (OFDMA) basestations allow multiple users to transmit simultaneously on different subcarriers during the same symbol period. This paper considers basestation allocation of subcarriers and power to each user to maximize the sum of user data rates, subject to constraints on total power, bit error rate, and proportionality
among user data rates. Previous allocation methods have been iterative nonlinear methods suitable for ofine optimization. In the special high subchannel SNR case, an iterative root-nding method has linear-time complexity in the number of users and
N logN complexity in the number of subchannels. We propose
a non-iterative method that is made possible by our relaxation
of strict user rate proportionality constraints. Compared to the
root-nding method, the proposed method waives the restriction
of high subchannel SNR, has signicantly lower complexity, and
in simulation, yields higher user data rates. Platform: |
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Author:王刚名 |
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Description: 本文提出一种基于多维定标的无线传感器网络三维定位算法,结合RSS经验衰减模型和最短路径建立相异性矩阵,采用轻量级矩阵分解算法降低相异性矩阵分解的计算复杂性,并利用网络中存在的周期性消息将初始定位信息回送,在后台使用迭代优化算法对初始定位结果求精。仿真实验表明,在测距误差一定的情况下,该算法能够提高节点三维坐标的初始计算精度,经过集中式的优化求精后与MDS-MAP算法相比,能够明显地提高节点三维定位的精度-This paper presents a method based on multidimensional scaling in wireless sensor network positioning algorithm, combined with RSS experience attenuation model and the shortest path to establish dissimilarity matrix, using lightweight matrix factorization algorithm reduces computational complexity dissimilarity matrix decomposition, and the use of the network in the presence of periodic messages will be the initial positioning information return, in the background using an iterative optimization the algorithm to the initial positioning results refinement. Simulation results show that, in some cases ranging error, this algorithm can improve the calculation accuracy of three-dimensional coordinates of the initial node, through the centralized optimization refinement after compared with MDS-MAP algorithm, which can obviously improve the precision of 3D Node Localization Platform: |
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Author:于文娟 |
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Description: 是推荐系统中最基础的MF实现程序，使用梯度下降的方法，用迭代优化的方式找到最优解-Recommendation system is the most basic MF realize procedure, using a gradient descent method, using an iterative optimization approach to find the optimal solution Platform: |
Size: 1024 |
Author:xiejin |
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Description: 粒子群寻优算法，pso，粒子群优化算法是一种进化优化技术，源于对鸟群扑食的行为，是一种基于迭代的优化工具。-Particle swarm optimization algorithm, pso, PSO is an evolutionary optimization technology, birds originated the prey' s behavior is based on an iterative optimization tools. Platform: |
Size: 2048 |
Author:筱玉 |
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Search - an iterative optimization