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parallel chaos optimization
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  “parallel chaos optimization”译为未确定词的双语例句
     A Hybrid Global Optimization Algorithm Based on Parallel Chaos Optimization and Simplex Search
     基于并行混沌和单纯形法的混合全局优化算法
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     By integrating simplex search the precision of parallel chaos optimization result is evidently improved.
     针对混沌在全局最优点附近搜索速度变得很慢、精度较低的缺点,结合单纯形法,提高了收敛的速度和求解精度。
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  相似匹配句对
     Parallel Chaos Differential Evolution Algorithm
     一种并行混沌差异演化算法
短句来源
     STUDY OF CHAOS IN PARALLEL-CONNECTED BUCK CONVERTERS
     并联BUCK变换器中的混沌研究
短句来源
     Research and Application of Parallel Chaos Particle Swarm Optimization
     并行混沌粒子群优化研究及应用
短句来源
     On Parallel Study
     关于平行研究
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     Chaos Engineering
     混沌工程学
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This paper presents a new hybrid global optimization algorithm based on parallel chaos optimization and simplex search. Parallel chaos optimization algorithm improves the efficiency of searching in the whole field by gradually shrinking the area of optimization variable. By integrating simplex search the precision of parallel chaos optimization result is evidently improved. Simulation results show that this hybrid algorithm is satisfactory.

混沌优化算法采用的是串行优化结构,采用并行结构进行,并不断缩小搜索空间,提高了混沌优化在变量取值范围较大情况下的搜索效率。针对混沌在全局最优点附近搜索速度变得很慢、精度较低的缺点,结合单纯形法,提高了收敛的速度和求解精度。仿真结果表明并行混合优化算法可以得到满意的结果。

>=Aimed at the modeling of complex system using feed-forward NN, this paper presents a new algorithm of parallel imitative scale optimization based on chaos variable. This algorithm reflects the chaotic variable to the range of variables optimized. The first is the raw searching of parallel chaos optimization in different chaotic trace. The second is elaborate searching by continually reducing the searching space of variable optimized and enhancing the searching precision. All measures make the searching...

>=Aimed at the modeling of complex system using feed-forward NN, this paper presents a new algorithm of parallel imitative scale optimization based on chaos variable. This algorithm reflects the chaotic variable to the range of variables optimized. The first is the raw searching of parallel chaos optimization in different chaotic trace. The second is elaborate searching by continually reducing the searching space of variable optimized and enhancing the searching precision. All measures make the searching result faster and more efficient to converge upon the optimal numerical value in the whole field. Simulation results of several testing functions demonstrate the effectiveness of the algorithm.

针对复杂系统多层前馈神经网络的建模问题,提出了一种基于混沌变量的并行变尺度优化算法.把混沌变量映射到优化变量区间,先采用并行混沌优化算法,在不同的轨道上进行“粗搜索”,然后不断缩小优化变量空间、不断加深优化变量的搜索精度进行“细搜索”,使神经网络权值参数能够更快、更有效地向全局最优解收敛。仿真结果表明该算法的有效性。

 
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