Titlebook: Advances in Swarm Intelligence; 4th International Co Ying Tan,Yuhui Shi,Hongwei Mo Conference proceedings 2013 Springer-Verlag Berlin Heide

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書目名稱Advances in Swarm Intelligence影響因子(影響力)




書目名稱Advances in Swarm Intelligence影響因子(影響力)學科排名




書目名稱Advances in Swarm Intelligence網絡公開度




書目名稱Advances in Swarm Intelligence網絡公開度學科排名




書目名稱Advances in Swarm Intelligence被引頻次




書目名稱Advances in Swarm Intelligence被引頻次學科排名




書目名稱Advances in Swarm Intelligence年度引用




書目名稱Advances in Swarm Intelligence年度引用學科排名




書目名稱Advances in Swarm Intelligence讀者反饋




書目名稱Advances in Swarm Intelligence讀者反饋學科排名

saphenous-vein

deficiency

Limerick

時代

Conception optimale de structures data, and then the particle swarm optimization algorithm is applied for piecewise area division and parameter optimization of the model. Simulation result shows that compared with traditional inversion method, better practicability and the higher significant wave height inversion precision are obtained by the proposed method.

津貼

,Introduction à l’optimisation de formes,e, and its model parameters is optimized by an improved PSO algorithm. The monthly runoff time series from 1953 to 2003 at Manwan station is selected as an example. The results show that the improved PSO has efficient optimization performance and the proposed forecasting model could obtain higher prediction accuracy.

Indolent

小淡水魚

Cask Theory Based Parameter Optimization for Particle Swarm Optimizationt can be used to search the tuned parameters such as inertia weight ., acceleration coefficients c. and c., and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.

Scleroderma

A Piecewise Linearization Method of Significant Wave Height Based on Particle Swarm Optimization data, and then the particle swarm optimization algorithm is applied for piecewise area division and parameter optimization of the model. Simulation result shows that compared with traditional inversion method, better practicability and the higher significant wave height inversion precision are obtained by the proposed method.

Gobble

Parameter Identification of RVM Runoff Forecasting Model Based on Improved Particle Swarm Optimizatie, and its model parameters is optimized by an improved PSO algorithm. The monthly runoff time series from 1953 to 2003 at Manwan station is selected as an example. The results show that the improved PSO has efficient optimization performance and the proposed forecasting model could obtain higher prediction accuracy.