例句與用法

1. In this paper the reasons for these drawbacks and the methods for overcoming these drawbacks are systemically studied from two levels , algorithm level and computing theory level
   本文從算法層和計算理論層兩個層次對造成這些缺陷的原因和克服這些缺陷的方法進行了系統的研究。
2. The proposed 64 bits high performance alu is optimized at algorithm level , logic level , circuit level and layout level , and is implemented in 0 . 18 m cmos process . furthermore , the testing technique of the alu is discussed . this thesis mainly contributes to the following aspect : 1
   文章從部件的算法、邏輯結構、電路參數、物理版圖等多個層次進行設計優化,在0 . 18 mcmos工藝下實現了一款64位高性能算術邏輯部件,并對該部件的測試方法進行研究。
3. The studies indicate that the algorithm level only deals with getting over the former two drawbacks of neural network learning using advanced optimization algorithms in the intrinsic framework of neural network , and great breakthrough is hard to made because of the limit of current optimization theory
   這一層次的研究表明，算法層只是在原有神經網絡的框架下利用高性能的優化算法克服網絡學習的前兩個缺陷，由于受目前優化理論的限制，很難有巨大的突破。
4. 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
   在算法層，本文對目前用于神經網絡訓練的各種算法，包括梯度算法、智能學習算法和混合學習算法進行了比較研究；對用于神經網絡訓練的bp算法的優化原理進行了詳細的理論分析，找到了bp算法存在嚴重缺陷的原因，并對其兩類改進算法-啟發式算法和二次梯度算法的優化原理，在統一的框架之下進行了詳盡的理論描述；對神經網絡全局優化算法主要是遺傳算法進行了詳細的闡述，并在此基礎上，設計了一種性能改進的遺傳算法；最后基于神經網絡學習的benchmark問題對各種算法在網絡訓練中的應用性能進行了仿真研究，并提出了遺傳算法受困于“維數災難”的觀點。