Titlebook: Advances in Soft Computing; 10th Mexican Interna Ildar Batyrshin,Grigori Sidorov Conference proceedings 2011 Springer-Verlag GmbH Berlin He

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書目名稱Advances in Soft Computing影響因子(影響力)




書目名稱Advances in Soft Computing影響因子(影響力)學科排名




書目名稱Advances in Soft Computing網(wǎng)絡公開度




書目名稱Advances in Soft Computing網(wǎng)絡公開度學科排名




書目名稱Advances in Soft Computing被引頻次




書目名稱Advances in Soft Computing被引頻次學科排名




書目名稱Advances in Soft Computing年度引用




書目名稱Advances in Soft Computing年度引用學科排名




書目名稱Advances in Soft Computing讀者反饋




書目名稱Advances in Soft Computing讀者反饋學科排名

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Bio-Inspired Optimization Methods for Minimization of Complex Mathematical Functionsintegrate the results, the proposed method is called FPSO+FGA. The new hybrid FPSO+FGA approach is compared with the Simulated Annealing (SA), PSO, GA, Pattern Search (PS) methods with a set of benchmark mathematical functions.

Preserve

Modeling Prey-Predator Dynamics via Particle Swarm Optimization and Cellular Automatams that change cells state according to some pre-defined heuristic, to diffusion algorithms based on the one dimensional Navier - Stokes equation or lattice gases. In this work we show a novel idea since the predator dynamics evolve through Particle Swarm Optimization.

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為敵

Matt Osborne,Richard Hawkins,John McDermidtheir computation is unusually complex. This makes us think that there should be an analog of Heisenberg’s uncertainty principle well known in quantum mechanics: when we an unusually beneficial situation in terms of results, it is not as perfect in terms of computations leading to these results. In

Terrace

婚姻生活

https://doi.org/10.1007/978-3-031-14835-4able to drive uncertainty like that arising from process measurements. The developed model is contrasted with an usual type-1 fuzzy model driven by the same uncertain data. Model development is conducted, mainly, by experimental data which is comprised by thirteen data sets obtained from different p

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Computer Safety, Reliability, and Security of the Sugeno type. In our case study the controller has four inputs, each of them with two membership functions; we consider the intersection point for every pair of membership functions as the main parameter and their individual shape as secondary ones in order to achieve the tuning of the fuzzy