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benchmark problem中文是什么意思

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用"benchmark problem"造句"benchmark problem"怎么读"benchmark problem" in a sentence

中文翻译手机手机版

  • 基准算题
  • 基准问题
  • 评价水准问题
  • 评效问题

例句与用法

  • In chapter 6 . we use five benchmark problems to show the experimental results of our proposed agent - based approach
    第六章给出了5个实际问题的实验结果。
  • Some benchmark problems are tested in the real space and showed the results . 2 . 2 dynamical optimization method
    3同时本文还给出了一种基于基因算法和传统优化算法相结合的途径解决连续的minimax优化问题。
  • Mackey - glass ( mg ) chaotic time series is one of benchmark problems in nonlinear time series forecast , which is representative
    Mackey - glass ( mg )混沌时间序列具有非线性特性,是时间序列预测问题中的基准问题之一,具有代表性。
  • In each sub - population , breeding and evaluation are implemented using multi - threaded mechanism . the benchmark problems numerical experiment results demonstrate that the proposed method can rapidly converge to the pareto optimal front and spread widely along the front
    在做了这两种方式的并行化以后不仅可以获得更好的计算性能，在pareto最优解集的优化效果上有更显着的改进。
  • The local search procedure based on several critical block based neighborhood structure is hybrid with aco _ stag algorithm , comparisons with other well - performed algorithms on taillard ' s benchmark problems show that the hybrid algorithm performs better
    给出了fiowshop问题的几种基于关键路径的邻域结构，并把蚁群算法与邻域搜索相结合构成混合算法，与其他算法在taillard流水作业调度测试问题集上的比较试验表明，混合算法性能更优。
  • It is an extension of core of manna and pnueli " s pltl . in succession , a formal axiom deductive system is presented . it ' s soundness based formal semantic defined in this thesis is proved . as an example , we specify grc ( generalized railroad crossing ) , which is a benchmark problem for real time systems , and verify it ' s safety and liveness
    作为它的一次实际应用，我们用它对于实时系统中的一个典型实例： grc ( generalizedrailroadcrossing )进行了描述，给出了它的系统规约，在此基础上，演绎式的证明了系统的一个安全性和活性命题。
  • In the algorithm level , currently various training algorithms of neural networks , including gradient algorithms , intelligent learning algorithms and hybrid algorithms , are comparatively studied ; the optimization principle of bp algorithm for neural networks training is analyzed in detail , and the reasons for serious disadvantages of bp algorithms are found out , moreover , the optimization principle of two kinds of improved bp algorithms is described in a uniform theoretic framework ; and the global optimization algorithms of neural networks , mainly genetic algorithm are expounded in detail , it follows that a improved genetic algorithm is proposed ; finally the training performances of various algorithms are compared based on a simulation experiment on a benchmark problem of neural network learning , furthermore , a viewpoint that genetic algorithm is subject to " curse of dimension " is proposed
    在算法层，本文对目前用于神经网络训练的各种算法，包括梯度算法、智能学习算法和混合学习算法进行了比较研究；对用于神经网络训练的bp算法的优化原理进行了详细的理论分析，找到了bp算法存在严重缺陷的原因，并对其两类改进算法-启发式算法和二次梯度算法的优化原理，在统一的框架之下进行了详尽的理论描述；对神经网络全局优化算法主要是遗传算法进行了详细的阐述，并在此基础上，设计了一种性能改进的遗传算法；最后基于神经网络学习的benchmark问题对各种算法在网络训练中的应用性能进行了仿真研究，并提出了遗传算法受困于“维数灾难”的观点。
用"benchmark problem"造句  

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