algorithm complexity造句
造句與例句
手機(jī)版
- At the same time , the algorithm complexities were also analyzed
該算法適用于時(shí)間表在現(xiàn)實(shí)環(huán)境中的各種應(yīng)用。 - Because of the characteristic of fft , a new recursion method is designed in the paper . and then the algorithm complexity is analyzed
最后,在圖像的編碼方面,本文選用了常見的哈夫曼編碼方法進(jìn)行并行算法的設(shè)計(jì)。 - Again , if the analysis engine knows if loading coils are allowed , and , if so , how they may be present , algorithm complexity can be eased
再次地，如果分析引擎知道是否允許負(fù)載線圈，如果有的話，他們是如何安裝的，即能降低算法復(fù)雜性。 - The major challenges of real - time implementation with digital signal processing techniques lie in the algorithm complexity and high data rate of radar
由于算法本身的復(fù)雜度和雷達(dá)數(shù)據(jù)的高速率，方位向處理的實(shí)時(shí)實(shí)現(xiàn)具有很大的挑戰(zhàn)性。 - Moreover , this theory can change the problem in non - linearity space to that in the linearity space in order to reduce the algorithm complexity by using the kernel function idea
又由于采用了核函數(shù)思想，使它把非線性空間的問題轉(zhuǎn)換到線性空間，降低了算法的復(fù)雜度。 - Model scale and algorithm complexity are also discussed in details . in order to decrease the scale and difficulty , a series of heuristic rules are adopted during solution
文中同時(shí)對模型的規(guī)模和算法的復(fù)雜性進(jìn)行了詳細(xì)的分析，為了降低問題求解的規(guī)模和難度，提出了一系列啟發(fā)式規(guī)則。 - In this article , the auther evaluated the mimimal path sets between two nodes with the conection matrix determinant , not the tranditional matrix square function , which greatly reduced the algorithm complexity
本文用聯(lián)絡(luò)矩陣的行列式運(yùn)算代替?zhèn)鹘y(tǒng)的矩陣乘方運(yùn)算來求網(wǎng)絡(luò)兩點(diǎn)之間正常的狀態(tài)集合，進(jìn)而求出網(wǎng)絡(luò)系統(tǒng)的可靠度，極大的減少了運(yùn)算的復(fù)雜性。 - For the case of all jobs with constant processing time , corresponding to the case when there are only a constant number of job release times and the general released case , we provide two optimal algorithms and analysis their algorithm complexity
對于所有工件的加工時(shí)間都相等的情況，分別對常數(shù)個(gè)到達(dá)時(shí)間和任意個(gè)到達(dá)時(shí)間的情況給出了兩個(gè)最優(yōu)算法，并給出了其算法復(fù)雜性。 - An alternate proof about its existence and uniqueness and its explicit represention are given , especially the rational interpolation formulas for simple konts and double konts , and the algorithm complexity in case of double konts is improved from o ( n ~ 2 ) to o ( n )
首先,介紹了cv ( cauchy - vandermonde )有理函數(shù)插值公式,給出了cv有理函數(shù)空間上插值問題解的存在唯一性定理的另一種簡單證明和顯式表示 - Moreover , this theory can change the problem in non - linearity space to that in the linearity space in order to reduce the algorithm complexity by using the kernel function idea . svm have become the hotspot of machine learning because of their excellent learning performance
由于采用了結(jié)構(gòu)風(fēng)險(xiǎn)最小化原則代替經(jīng)驗(yàn)風(fēng)險(xiǎn)最小化原則，使得較好的解決了小樣本學(xué)習(xí)的問題；又由于采用了核函數(shù)思想，使它把非線性空間的問題轉(zhuǎn)換到線性空間，降低了算法的復(fù)雜度。 - It's difficult to see algorithm complexity in a sentence. 用algorithm complexity造句挺難的
- ( 2 ) with the basis of ( 1 ) , the thesis present a improved hough transform algorithm based on grc , it turns out its improvement on algorithm complexity 、 accuracy and its useness . ( 3 ) with the deeply analysis on the problem existed in a license plate locating algorithm , which based on the extraction of parallelogram , and
( 3 )在車牌定位算法中,在實(shí)驗(yàn)的基礎(chǔ)上深入分析了基于平行四邊形提取的車牌定位算法中存在的問題,并探討了如何用粒度計(jì)算解決這些問題。 - The discussion of main parallel technologies on construction of parallel sliq algorithm is presented in this paper . the computing result of algorithm complexity of sequential and parallel algorithm indicates : when the data set is large enough , as to continuous attributes , the parallel algorithm almost get speedup value equal to the number of processors ， while as to categorical attribute the improvement of parallel algorithm is limited
通過對串行和并行算法時(shí)間復(fù)雜度的計(jì)算表明，當(dāng)數(shù)據(jù)集充分大時(shí)，由于連續(xù)屬性的排序計(jì)算操作分散到各個(gè)處理機(jī)單元上進(jìn)行，顯著降低了計(jì)算時(shí)間，從而可以得到近似于處理機(jī)個(gè)數(shù)的加速比，對于離散屬性，本并行算法對串行算法的性能提高有限 - Considering the np - complete problem , how to get the approximate optimized scheme of job - shop scheduling , and aimed at improving the efficiency of products and taking good advantage of concurrence , asynchronism , distributing and juxtaposition in multi - products and devices processing , we could divide the working procedures into the attached one which has the only precursor and subsequence and unattached one by analyzing working flow chart of job - shop , that is the working procedures are divided into two types , then the bf and the ff methods about memory scheduling in os are applied , therefore a new approximate optimized scheme is presented in the paper which could solve the common job - shop scheduling . namely , the acpm and the bfsm are applied to the classified and grouped working procedures considering the compact of the procedures and practical examples approved it . the results we analyzing and tested show that it is better than the heuristic algorithm common used , for less restriction terms , more satisfying algorithm complexity and better optimized results
針對job - shop調(diào)度問題求最優(yōu)解算法這一npc問題，本文以充分發(fā)揮多產(chǎn)品、多設(shè)備加工所具有并發(fā)性、異步性、分布性和并行性的加工優(yōu)勢，從而提高產(chǎn)品的加工效率為目標(biāo)，對job - shop調(diào)度問題的工藝圖進(jìn)行適當(dāng)分解，使工序在一定時(shí)間段或是為具有唯一緊前、緊后相關(guān)工序或是為獨(dú)立工序，即將工序分兩類，再結(jié)合操作系統(tǒng)中內(nèi)存調(diào)度的最佳適應(yīng)( bf )調(diào)度方法和首次適應(yīng)( ff )調(diào)度方法的先進(jìn)思想，通過分析提出了一種解決一般job - shop調(diào)度問題的全新近優(yōu)解方案：在考慮關(guān)鍵設(shè)備上工序盡量緊湊的前提下，將工序分類、對這兩類工序分批采用擬關(guān)鍵路徑法( acpm )和最佳適應(yīng)調(diào)度方法( bfsm )安排工序的算法，用實(shí)例加以驗(yàn)證，并給出結(jié)果甘特圖。 - Complexity parameter c2 , fluctuation complexity parameter cf and algorithm complexity parameter c ( n ) have been adopted to analysis pressure fluctuation signals from a gas - solid fluidized bed , the variation of the three complexity parameters with gas velocity from fixed bed , bubbling fluidization to turbulence fluidization are studied , then compared in order to characterize regime behaviors . it is demonstrated that there exists a phenomenon named " recorded " in the regime transition from fixed bed to bubbling fluidization , the three complex parameters are believed to be a new technique for flow regime identification
結(jié)果表明，起始流化至鼓泡態(tài)轉(zhuǎn)變的過程中，復(fù)雜性參數(shù)表明氣固體系會(huì)進(jìn)行一種所謂的“重構(gòu)”現(xiàn)象，這與用k熵表征起始流化態(tài)至鼓泡態(tài)之間變化時(shí)所得到的結(jié)果相一致，并能明確地指示固定床、鼓泡流化及湍動(dòng)流化等不同流態(tài)之間的轉(zhuǎn)變過程，為流型識(shí)別提供了新思路。
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