PSO
美 
英
- 網絡粒子群優化算法(Particle Swarm Optimization)；粒子群算法；微粒群算法
例句
The experiment results showed that this approach has advantages of both PSO and FLANN, meanwhile it has better precision.
實驗結果表明，該方法結合了PSO和FLANN兩者的優點，建模精度高。
Although very easy to implement, this hybrid SM-PSO is an efficient way to locate global optima of continuous multimodal functions.
算法實現簡單，具有很高的可靠性，是一種求解多峰連續函數極值的有效方法。
It used to solve the problem that Particle Swarm Optimization(PSO) easily falls into a local extremum.
克服了經典粒子群算法中參數選擇問題以及粒子群算法易陷入局部極值問題。
this document is mainly targeted at PSO algorithm in Matlab environment application to the preparation of that document m!
本文件主要是針對于粒子群算法在matlab環境中地應用說編寫地m文件！
And, in FNN weight training, improved PSO in the convergence rate and the ability to jump out to local optimum algorithm is better than BP.
且改進的粒子群算法在模糊神經網絡權值的訓練中收斂速度和跳出局部最優的能力都要比BP算法更優。
To perform the parallel optimization, the comparison used in tournament selection was employed to compare the searched solutions of the PSO.
采用聯賽選擇算子比較粒子群算法所搜索到的解。
Task assignment problem is a typical NP problem. Particle Swarm Optimization (PSO) algorithm was used to solve task assignment problem.
任務指派問題是典型NP難題，引入粒子群優化算法對其進行求解。
Finally, this optimization algorithm is proved to be effective by an example.
結合一個仿真算例，表明了采用基于PSO的軍械調運決策優化算法的有效性。
To this problem, this paper proposed one kind of method to choose the parameters of the SVM by particle swarm optimization algorithm (PSO).
針對此問題，提出一種基于粒子群優化算法的支持向量機參數選擇方法。
This variant of PSO enables the diversity of the swarm to be preserved to discourage premature convergence.
由于該方法能夠保持群體的多樣性，因此可以避免早熟收斂。
This new algorithm applies particle swarm optimization (PSO) to design the CNN templates to identify the edge of a nucleated cell.
該方法運用粒子群優化算法設計CNN模板，利用CNN對骨髓有核細胞進行邊緣檢測。
The PSO attribute reduction algorithm ought to improve the efficiency, however, it has the premature convergence problem.
粒子群（PSO）屬性約簡算法，雖然可提高求解效率，但易陷入局部最優。
Subsequently, this paper focuses on PSO optimization model and its applications in RFID Tags and Antenna Simulation Deployment System.
隨后，本文重點介紹了粒子群算法模型及其在RFID標簽天線仿真部署系統中的應用。
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function.
微粒群優化算法是求解連續函數極值的一個有效方法。
It transformed the parameter selection problem into functional optimization problem by creating a function of the PSO property parameters.
針對特定問題，將PSO方法的性能表示成參數的函數，從而將參數選擇問題轉變成函數優化問題。
Uses multi- populations cooperation optimization algorithm which PSO develops, good article. May have a look to use.
詳細說明：使用PSO開發的多種群協作優化算法，好文章啊。可以看看采用啊。
Particle swarm optimization (PSO) is a good inversion method with characterizing simple algorithm, fast convergence, and easy operation.
粒子群優化算法是一種很好的優化反演方法，具有算法簡單、收斂較快、容易實現等特點；
Another is to examine faults of asynchronous Motors in terms of BP neural network based on Particle Swarm Optimization(PSO).
二是利用基于粒子群算法（PSO）優化的BP神經網絡進行異步電機故障診斷。
The simulation results show that the improved PSO algorithm can solve the high-dimensional numerical optimization problem effectively.
實驗結果表明該改進微粒群算法可以有效地解決高維數值優化問題。
The computational results show that the PSO is a viable and effective approach to solve the semiconductor furnace batch scheduling problem.
實例計算的結果表明，該算法是解決半導體爐管區調度問題可行且高效的方法。
Aiming to solve out the pre-mature convergence phenomenon, the chaos mutation is introduced into PSO for improving global optimal ability.
混沌變異機制引入到PSO算法中，克服了進化過程中出現的早熟收斂現象，改進了PSO算法的全局尋優能力；
Then, the problem was solved by particle swarm optimization algorithm (PSO) after elimination of the constraint equations.
然后針對分解后的子問題，利用微粒群優化算法（PSO）求解。
Results show that the improved PSO algorithm has a great improve in global search capability and solution precision.
實驗表明改善后的算法的求解精度和全局搜索能力得到較大的提高。
The theoretical analysis and experimental results indicate that the proposed niche PSO algorithm is feasible and effective.
理論分析及實驗結果表明，該算法是有效可行的。
Summary of Background Data. PSO is a technique popularized in the lumbar spine primarily for the correction of fixed sagittal imbalance.
研究背景概述：PSO是一種廣泛用于腰椎矢狀面失衡矯正的主要技術。
Application examples show that it is feasible to apply the improved PSO to the weight solution of power load combination forecasting model.
通過應用實例證明，將改進的粒子群優化算法應用到電力負荷組合預測模型的權重求解是可行的。
At last, individual decision-making PSO is applied into solving nonlinear equations problem, simulation results show they are more superior.
最后把個體決策微粒群算法應用到非線性方程組求解問題中，仿真結果表明它們具有較大的優勢。
By adopting a dynamic inertia gene and condensed network structure, this article improve BP-PSO algorithm, efficiently solve these problems.
因此，本文采用一種動態慣性因子并精簡網絡結構的改進BP-PSO算法，有效解決這些問題。
This paper adds mutation operator to adaptive PSO and apply it in the lymphoma morphology parameter classifier problems.
對自適應粒子群算法引入變異算子，并對其進行改進，將其應用到淋巴瘤形態參數的分類問題上。
Particle swarm optimization (PSO) algorithm is easy to be trapped into local minima in optimizing multimodal function.
針對利用粒子群優化算法進行多極值點函數優化時，存在陷入局部極小點和搜尋效率低的問題。
PSO clustering algorithm is known to have simple parameters and fast convergence, but there are also local optimal problems.
粒子群優化聚類算法具有參數簡單，收斂快等優勢，但也有局部極值問題。
Considering the stronger search ability of Particle Swarm Optimization(PSO), this paper introduces a PSO covering algorithm.
為此，結合粒子群優化（PSO）具有的全局搜索能力，提出一種PSO覆蓋算法。
During the search process of PSO, the chaotic local optimizer was introduced to raise its resulting precision and convergence rate.
然后，在PSO的搜索過程中引入混沌局部搜索策略，來提高解的精度和收效速度。
Adaptive perceptive ability is assigned to particles in PSO for balancing their global and local searching and avoiding prematurity.
通過為粒子賦予自適應感知能力，算法能較好地平衡全局和局部搜索，且有能力跳出局部極值，防止早熟。
The parameters and thresholds of classifiers are optimized by improved Particle Swarm Optimization(PSO) algorithm.
改進的粒子群優化算法全局搜索BP神經網絡的權值和閾值。
Finally, we use an engineering example with PSO arithmetic to realize optimization of the machine tool basic shaft.
最后根據機床主軸優化設計的實例采用PSO算法，實現了對機床主軸結構的優化設計。
If some particles trended to local extremum in PSO algorithm implementation, the particle velocity was updated and re-initialized.
在PSO算法的運行過程中，對有集聚傾向的粒子進行速度變異處理，重新初始化速度。
Particle Swarm Optimization (PSO for short) is a evolutionary algorithm with simple operations and few parameters.
粒子群優化算法（PSO）是一種進化算法，操作簡單，參數少。
Emulation experiments demonstrated that the modified algorithm improves the PSO's global search capability remarkablely.
仿真實驗表明，改進的粒子群算法顯著提高了PSO算法的全局搜索能力。
This paper presents a survey of the PSO on Project of Power Grid Construction and Irrigation, and provides some outlook.
本文分析了目前應用于電網構建和農田水利灌溉等工程中的微粒群算法，并提出了一些展望。
