例句與造句

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(jì)算理論層兩個(gè)層次對(duì)造成這些缺陷的原因和克服這些缺陷的方法進(jìn)行了系統(tǒng)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(jié)構(gòu)、電路參數(shù)、物理版圖等多個(gè)層次進(jìn)行設(shè)計(jì)優(yōu)化,在0 . 18 mcmos工藝下實(shí)現(xiàn)了一款64位高性能算術(shù)邏輯部件,并對(duì)該部件的測(cè)試方法進(jìn)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(jīng)網(wǎng)絡(luò)的框架下利用高性能的優(yōu)化算法克服網(wǎng)絡(luò)學(xué)習(xí)的前兩個(gè)缺陷，由于受目前優(yōu)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(duì)目前用于神經(jīng)網(wǎng)絡(luò)訓(xùn)練的各種算法，包括梯度算法、智能學(xué)習(xí)算法和混合學(xué)習(xí)算法進(jìn)行了比較研究；對(duì)用于神經(jīng)網(wǎng)絡(luò)訓(xùn)練的bp算法的優(yōu)化原理進(jìn)行了詳細(xì)的理論分析，找到了bp算法存在嚴(yán)重缺陷的原因，并對(duì)其兩類改進(jìn)算法-啟發(fā)式算法和二次梯度算法的優(yōu)化原理，在統(tǒng)一的框架之下進(jìn)行了詳盡的理論描述；對(duì)神經(jīng)網(wǎng)絡(luò)全局優(yōu)化算法主要是遺傳算法進(jìn)行了詳細(xì)的闡述，并在此基礎(chǔ)上，設(shè)計(jì)了一種性能改進(jìn)的遺傳算法；最后基于神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)的benchmark問題對(duì)各種算法在網(wǎng)絡(luò)訓(xùn)練中的應(yīng)用性能進(jìn)行了仿真研究，并提出了遺傳算法受困于“維數(shù)災(zāi)難”的觀點(diǎn)。
5. It's difficult to find algorithm level in a sentence. 用algorithm level造句挺難的