非常好的優(yōu)化算法的書(shū)，詳細(xì)介紹了蟻群算法和粒子群算法以及相關(guān)的matlab工具箱，講了理論和應(yīng)用給出了工具箱的下載地址。 Swarm intelligence is an innovative computational way to solve hard problems. In particular, PARTICLE swarm optimization, also commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the PARTICLE discovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by PARTICLEs in multidimensional space that have two characteristics: a position and a velocity. These PARTICLEs wander around the hyperspace and remember the best position that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions.
標(biāo)簽： 優(yōu)化算法
上傳時(shí)間： 2014-01-26
上傳用戶(hù)：zgu489
％ PURPOSE : Demonstrate the differences between the following filters on the same problem: ％ ％ 1) Extended Kalman Filter (EKF) ％ 2) Unscented Kalman Filter (UKF) ％ 3) PARTICLE Filter (PF) ％ 4) PF with EKF proposal (PFEKF) ％ 5) PF with UKF proposal (PFUKF)
標(biāo)簽： the Demonstrate differences following
上傳時(shí)間： 2016-10-20
上傳用戶(hù)：wuyuying
無(wú)線傳感器網(wǎng)絡(luò)，粒子濾波，PARTICLE filter for sensor network
標(biāo)簽： 無(wú)線傳感器網(wǎng)絡(luò)
上傳時(shí)間： 2016-11-14
上傳用戶(hù)：firstbyte
將PSO和LBG結(jié)合在一步迭代過(guò)程中，并使用PARTICLE-pair（PP）搜索問(wèn)題空間的算法
上傳時(shí)間： 2014-01-22
上傳用戶(hù)：zmy123
dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and PARTICLE filters and smoothers, as well as useful classes such as common probability distributions and stochastic processes.
標(biāo)簽： probabilistic distributed large-scale dynamical
上傳時(shí)間： 2014-01-12
上傳用戶(hù)：wangdean1101
《Optimal State Estimation - Kalman, H Infinity, and Nonlinear Approaches》 一書(shū)的配套源碼，包括了Kalman Filter、Hinf Filter、PARTICLE Filter等的Matlab源碼
標(biāo)簽： Estimation Approaches Nonlinear Infinity
上傳時(shí)間： 2013-12-20
上傳用戶(hù)：caozhizhi
evolution computing 現(xiàn)在最火的一篇論文 Handling Multiple Objectives With PARTICLE Swarm Optimization
上傳時(shí)間： 2016-07-01
上傳用戶(hù)：白水煮瓜子
PARTICLE-swarm-optimization-PSO-for-MPPT-master
標(biāo)簽： SailingSim-Matlab-master
上傳時(shí)間： 2017-03-11
上傳用戶(hù)：zosoong
在微電網(wǎng)調(diào)度過(guò)程中綜合考慮經(jīng)濟(jì)、環(huán)境、蓄電池的 循環(huán)電量，建立多目標(biāo)優(yōu)化數(shù)學(xué)模型。針對(duì)傳統(tǒng)多目標(biāo)粒子 群算法(multi-objective PARTICLE swarm optimization，MOPSO) 的不足，提出引入模糊聚類(lèi)分析的多目標(biāo)粒子群算法 (multi-objective PARTICLE swarm optimization algorithm based on fuzzy clustering，F(xiàn)CMOPSO)，在迭代過(guò)程中引入模糊聚 類(lèi)分析來(lái)尋找每代的集群最優(yōu)解。與 MOPSO 相比， FCMOPSO 增強(qiáng)了算法的穩(wěn)定性與全局搜索能力，同時(shí)使優(yōu) 化結(jié)果中 Pareto 前沿分布更均勻。在求得 Pareto 最優(yōu)解集 后，再根據(jù)各目標(biāo)的重要程度，用模糊模型識(shí)別從最優(yōu)解集 中找出不同情況下的最優(yōu)方案。最后以一歐洲典型微電網(wǎng)為 例，驗(yàn)證算法的有效性和可行性。
標(biāo)簽： 模糊 模型識(shí)別 微電網(wǎng) 多目標(biāo)優(yōu)化 聚類(lèi)分析
上傳時(shí)間： 2019-11-11
上傳用戶(hù)：Dr.趙勁帥
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