收效神速 的英文怎麼說

中文拼音 [shōuxiàoshénsù]
收效神速 英文
obtain instant results
  • 收 : Ⅰ動詞1 （把攤開的或分散的事物聚集、合攏） put away; take in 2 （收取） collect 3 （收割） harvest...
  • 效 : Ⅰ名詞（效果; 功用） effect; efficiency; result Ⅱ動詞1 （仿效） imitate; follow the example of 2 ...
  • 神 : Ⅰ名詞1 （神靈） god; deity; divinity 2 （精神; 精力） spirit; mind 3 （神氣; 神情） expression; l...
  • 速 : Ⅰ形容詞（迅速; 快） fast; rapid; quick; speedy Ⅱ名詞1 （速度） speed; velocity 2 （姓氏） a surna...
  • 收效 : yield results; produce effects; bear fruit
  • 神速 : marvellously quick; with amazing speed
  1. This article puts forward a solution named divide - assemble by deducing the size of bp neural network to overcome entering the local best point, the dividing process is that a big bp neural network is divided into several small bp neural networks, every small bp neural network can study alone, after all small bp neural networks finish their study, we can assemble all these small bp neural networks into the quondam big bp neural networks ; on the basis of divide - assemble solution, this article discusses the preprocessing of input species and how to deduce the size of bp neural network further to make it easy to overcome entering the local best point ; for the study of every small bp neural network, this article adopts a solution named gdr - ga algorithm, which includes two algorithms. gdr ? a algorithm makes the merits of the two algorithms makeup each other to increase searching speed. finally, this article discusses the processing of atm band - width distribution dynamically

    本文從bp網的結構出發，以減小bp神經網路的規模為手段來克服陷入局部極小點，提出了bp神經網路的拆分組裝方法，即將一個大的bp網有機地拆分為幾個小的子bp網，每個子網的權值單獨訓練，訓練好以後，再將每個子網的單元和權值有機地組裝成原先的bp網，從理論和實驗上證明了該方法在解決局部極小值這一問題時是有效的；在拆分組裝方法基礎上，本文詳細闡述了輸入樣本的預處理過程，更進一步地減小了bp網路的規模，使子網的學習更加容易了；對于子網的學習，本文採用了最速梯度? ?遺傳混合演算法（即gdr ? ? ga演算法） ，使gdr演算法和ga演算法的優點互為補充，提高了收斂速度；最後本文闡述了用以上方法進行atm帶寬動態分配的過程。
  2. 1, q 3, and at last prove the exisitence of ( q, m + n, n, m ) resilient functions when n > q ? 1. intelligentized ids methods, which can make the system more adaptability and self - studying, are important research directions of ids so far. in order to make the ids systems have better identifying ability and efficiency against new intrusions, we propose the intrusion feature extra - ction algorithm based on ikpca by studying the different kinds of intrusion detection feature extraction algorithm based on unsupervised learning, and then theoretically analysis the conver - gence of the algorithm. in addition, we validate the validity of the algorithm by means of experim - ents ; at the same time, through studying ica and neural networks, we propose fastica - nn ids, and then test the kddcup99 10 % date set to make comparison of kpca 、 ikpca and fastica algorithms in intrusion detection advantages and disadvantages

    為了使入侵檢測系統對新的入侵行為有更好的識別能力和識別效率，本文在研究了各種基於無監督學習的入侵檢測特徵提取方法的基礎上，提出了基於增量核主成份分析（ ikpca ）的入侵檢測特徵提取方法，並對該方法進行了收斂性分析，同時結合模擬試驗對其正確性進行了驗證;另外，本文通過研究獨立成份分析和神經網路，提出了基於快速獨立成份分析和神經網路的入侵檢測方法（ fastica - nnids ） ，並通過對kddcup99的10 %數據集的檢測比較了核主成份分析（ kpca ） 、增量核主成份分析（ ikpca ）和快速獨立成份分析（ fastica ）在入侵檢測特徵提取方面的優缺點。
  3. Rapidly active skin cells, promote the circulation of the blood, remove fatigue of the skin, help the penetrate and the obsorb of the nourish element, whiten, moisturize and firm the eye skin, recovery the elastic of the skin, release the black eye circle

    功效：能迅速活化肌膚細胞，促進血液循環，消除肌膚疲勞，幫助營養成份滲透，吸收，美白滋潤及收緊眼部皮膚，恢復肌膚彈性，白皙細膩，改善眼周的血液循環，減淡黑眼圈，令雙眼時刻保持健康神采，展現柔滑明眸。
  4. In the last of this paper we apply our algorithms to the learning of feed - forward neural network, and get some new learning algorithms. we also give some numerical experiments to compare our algorithms with others

    最後，將得到的這些優化加速收斂方法應用到了多層前饋神經網路的學習過程，給出了加速收斂的bp演算法，通過實際神經網路學習問題驗證了工作的成效。
  5. The fish therapy can clean off the bacteria from your skin by kissing you and improve your immunity and relax your soul and body by stimulating your nerve

    溫泉魚通過親吻您的肌膚，啄食人體老化皮質、細菌和毛孔排泄物，刺激您的末梢神經，從而達到讓人體毛孔暢通，更好地吸收溫泉水中的多種礦物質，加速人體新陳代謝，增強免疫力和放鬆身心的功效。
  6. The fish therapy can clean off the bacteria from your skin by kiss your skin and improve your immunity and relax your soul and body by stimulating your nerve

    溫泉魚通過親吻您的肌膚，啄食人體老化皮質、細菌和毛孔排泄物，刺激您的末梢神經，從而達到讓人體毛孔暢通，更好地吸收溫泉水中的多種礦物質，加速人體新陳代謝，增強免疫力和放鬆身心的功效。
  7. Because ga possesses the traits of can global random search, the robustness is strong, been use briefly and broadly, it didn ’ t use path search, and use probability search, didn ’ t care inherence rule of problem itself, can search the global optimum points effectively and rapidly in great vector space of complicated, many peak values, cannot differentiable. so it can offset the shortages of nn study algorithm, can reduce the possibility that the minimum value get into local greatly, the speed of convergence can improve, interpolation time shorten greatly, the quantity of training reduce

    因為遺傳演算法具有全局隨機搜索能力，魯棒性強、使用簡單和廣泛的特點，它不採用路徑搜索，而採用概率搜索，不用關心問題本身的內在規律，能夠在復雜的、多峰值的、不可微的大矢量空間中迅速有效地尋找到全局最優解，所以可以彌補神經網路學習演算法的不足，使陷入局部最小值的可能性大大減少，使得收斂速度提高，訓練量減小。
  8. Finally, take example for a non - linear function, method mentioned in this paper is used to design wavelet neural network to approximate this function. the computer simulations confirm the method that is brought out in this paper is useful, and prove that wavelet neural network has not only fast convergence and better precision of approximation, but also good capability of forecasting and escaping error

    最後，對於一個實際的非線性函數，用本文介紹的方法來設計小波神經網路來逼近函數，模擬結果表明該方法的有效性，並且表明小波神經網路在函數逼近上，網路的收斂速度快，逼近精度高的特點，並且網路具有很好的泛化能力和容錯性。
  9. The artificial neural net ( ann ) way is universal regard as one of the most effective ways of stlf. in this paper, some research is developed for stlf using ann ways in several parts : the first part is about the arithmetic of ann based on bp model, namely the advanced of traditional bp arithmetic, one alterable step and scale bp arithmetic based on comparability of model and probability of accepting bp arithmetic is used to enhances a lot the convergence rate of learning process of bp network, but also avoid the stagnation problem to some extent. it indicates that the ann ' s efficiency and precision by the way can be ameliorated by the simulation of real data

    神經網路方法在短期預測中已經被公認為較有效的方法，本文針對神經網路用於電力系統短期負荷預測的幾個方面展開研究工作：第一部分研究一般用於負荷預測的神經網路bp模型的演算法，即對傳統的bp演算法的改進，將一種基於模式逼近度和接受概率的變步長快速bp演算法應用到短期負荷預測，模擬結果表明該方法有效的改善了bp演算法收斂速度慢以及容易陷入局部最小點的缺點，從而提高了神經網路用於負荷預測的效率和精度。
  10. Based on the discussions of the conventional and recent methods of short term load forecasting such as time series, multiple regression approaches and artificial intelligence technologies, this paper presents a hybrid short term forecasting model which combines the artificial neural network ( ann ) and genetic algorithm ( ga ). in order to improve the convergence speed and precision of the back - propagation ( bp ), a new improved algorithm - the adapted learning algorithm based on quasi - newton method is given

    本文首先分析比較了電力系統短期負荷預測的傳統方法時間序列法和回歸方法以及最近的專家系統和神經網路技術的優點和不足，然後針對人工神經網路bp演算法的不足對其進行了改進，採用了基於擬牛頓的自適應演算法，它提高了網路學習效率，具有較快的收斂速度和較高的精度。接著提出了改進的遺傳演算法來改善神經網路的局部收斂性。
  11. Their learning and training rules have been analyzed profoundly and their abilities to approximate arbitrary nonlinear function have been testified and compared by the simulation. a new rbf neural network has been presented which uses a raised - cosine function as activation transfer function. it provides a wider generalization in comparison with gaussian rbf neural networks by simulation as well as strong approximation ability, fast convergence, a rule to select the parameters of the networks

    本文詳細研究了兩種典型的前向神經網路（ bp網路和rbf網路）的學習和訓練演算法，提出了一種新穎的基於緊支集餘弦函數的徑向基神經網路，其克服了常用的高斯型rbf神經網路雖具有緊支集但各基函數非正交的不足，其收斂速度快、網路參數選取有理論依據且相比于高斯型rbf神經網路具有更強的泛化能力，模擬驗證了其有效性。
  12. The number of the hidden layers of mul - tilayer perceptrons ( mlps ) is analyzed, and three - layer perceptrons neural network is adopted ; by analyzing the mechanism of the neural cells in hidden layer, a method for combining genetic algorithm and bp algorithm to optimize the design of the neural networks is presented, and it solves the defects of getting into infinitesimal locally and low convergence efficiently, it can also solve the problem that it can usually obtain nearly global optimization solution within shorter time through using genetic algorithm method lonely ; several examples validate that this algorithm can simplify the neural networks effectively, and it makes the neural networks solve the practical problem of fault diagnosis more effectively

    對多層感知器隱層數進行了分析，確定採用三層感知器神經網路;通過對隱層神經元作用機理的分析，引入了遺傳演算法與bp演算法相結合以優化設計神經網路的方法，有效地解決了bp演算法收斂速度慢和易陷入局部極小的弱點，還可以解決單獨利用遺傳演算法往往只能在短時間內尋找到接近全局最優的近優解的問題;通過實例驗證了這種演算法能夠有效地簡化神經網路，使神經網路更加有效地解決實際的故障診斷問題。
  13. The simulation tests result indicates that the speed and precision of sample training are increased because of sample clustering for fuzzy modular networks. and the problem of slow training speed and local minimum point are avoided when bp networks are applied in the fault diagnosis of complex boiler

    本文所建的用於鍋爐故障診斷的模糊模塊化神經網路模型因進行了樣本聚類，實驗結果表明：其網路訓練的速度和精度明顯提高，同時有效地解決了bp網路應用於復雜的鍋爐系統故障診斷時，存在訓練收斂慢並容易陷入局部最小點的問題。
  14. The simulation results indicate the capability of genetic algorithm in fast and steady learning of neural networks, guaranteeing a global convergence and overcoming some shortcomings of traditional error back propagation algorithms, meanwhile prove that this neural networks adaptive control structure is effective to many control problems and it is easy for us to programme and employ the method in the practical system

    模擬結果表明遺傳演算法能夠快速穩定地學習神經網路，保證全局收斂西安理工大學碩士學位論文並且能夠克服傳統誤差反傳演算法的一些缺點，也證明了這種神經網路自適應控制結構可以有效解決系統中存在的控制難題，同時編程容易，便於在實際系統中應用。
  15. The result of fault diagnosis simulation tests indicates that the fault diagnosis system could make training error reach to the aim value quickly and efficaciously for all pulverizing system fault samples. at the same time, simulation tests prove that the fault samples with the signal of zero or one of pulverizing system and bp neural network model are correct, and this system faults can be diagnosed exactly and quickly. obviously, this research is successful and lay the foundation for the development of pulverizing fault diagnostic system

    其故障診斷模擬實驗結果表明，應用本文所開發研究的制粉系統各故障樣本及其相關故障樣本訓練時均能快速有效地收斂於一個設定的系統誤差值；同時其故障診斷的模擬實驗證明了本文所建立的以0 、 1為徵兆量的制粉系統故障樣本和bp神經網路模型是正確的，且能快速、較準確地對故障情況作出判斷，顯然，本文的工作是有成效的，為制粉系統故障系統進一步開發奠定了基礎。
  16. By studying the discrete fourier transform properties of the band - limited digital signal, the authors introduce alternating projection neural networks into the paper, expand apnn ' s application scope from real field to complex field, and present several important conclusions on apnn. analyzing and discussing network ' s tolerance to noise, convergence rate and the spectral leakage problem of the truncated signal expected to be extrapolated by using these conclusions, the paper presents an extrapolation algorithm for band - limited signals based on alternating projection neural networks. a lot of simulation experiments show that the algorithm is effective. in addition, the algorithm is also effective to spectrum extrapolation. owing to adopting network structure, the algorithm is prone to parallel computation and vlsi design, and consequently can satisfy real time military processing needs

    本文通過對頻帶受限數字信號的離散傅立葉變換特性的研究，引進了交替投影神經網路，並將其應用范圍從實數域拓廣到復數域，且給出了在復數域仍然成立的若干結論.運用這些結論，在對網路噪聲抑制、網路收斂速度及待外推信號因截斷而造成頻譜嚴重外泄問題的分析與討論的基礎上，提出了一種基於交替投影神經網路的外推演算法.模擬實驗表明該方法是行之有效的.另外，該演算法對頻譜外推同樣適用；由於它採用全互連神經網路結構，易於并行計算和vlsi實現，從而可滿足軍事上實時處理的需要
  17. The improving neural network subsection prediction model can take advantage of simple network structure, fast convergence rate and strong generalization capability, and get a good modeling effect

    尤其是改進的神經網路分段預測模型具有網路結構簡化、收斂速度快，泛化能力強的特點，取得很好的擬合精度和預測效果。
  18. Identifying lithology according to logging data by self - organized neural network not only has stronger self - organizability, adaptability and fault - tolerance, but also has small calculation and gets quick returns compared with other methods of netural network

    摘要利用自組織神經網路對測井數據進行巖性識別，具有較強的自組織性、適應性和較高的容錯能力，與其他神經網路演算法相比，計算量小、收效速度快。
  19. Later on, after elaborating the disadvantages of the old methods in detecting and recognizing moving objects, a series of corresponding approaches are proposed, such as grid scan, local tracking bug and dynamic window in object tracing to reduce the huge data needed to be processed, maximum and minimum for selecting a proper segmentation threshold and improved conversion from rgb model to hsv and so on to decrease the influence of inhomogeneous lighting and the color noise, a bilinear interpolation in each quadrant to eliminate the bad effect on the recognition precise because of the distortions of the camera. after that, much emphasis is given on application study in pattern recognition with a feed - forward neural network. both the basic bp algorithm and improved bp algorithm in the study process are described in detail, and the later is used to quicken convergence speed and improve validity of the network

    然後，分析和闡明了傳統的運動目標檢測方法的不足，並在此基礎上結合研究中的實際實驗環境，提出了一系列解決方法，包括針對降低龐大數據量而提出的網格掃描、局部「跟蟲」追蹤和動態窗口掃描等目標檢測方法，針對實驗環境中光照不均和顏色干擾提出基於人機交互的最大最小值閾值選取方法和引入改進的rgb模型到hsv模型的轉換方法，為消除圖像畸變對識別精度的惡劣影響而採用的通過控制點進行雙線性插值進行畸變校正的方法；緊接著，概述了神經網路的發展歷史和幾種常用神經網路模型的特點，重點研究了前饋型神經網路在模式識別中的應用問題，詳細闡述了基本的bp演算法和學習過程中bp演算法的改進，從而使網路收斂速度更快，解決問題更有效，並在此基礎上，設計了一個基於bp神經網路的運動目標識別系統，給出了實驗結果。
  20. The software in which the neural network is realized is designed in the paper, and with two - link planar manipulator, the feasible and effective of using ga to learn the weights of nn is studied, simulations show that the proposed method improves considerably the inverse kinematic solutions for robot manipulator and guarantees a rapid global convergence

    本文對用軟體實現該神經網路進行了程序設計，並用兩桿平面機械手對應用遺傳演算法學習神經網路的權系數的可行性和有效性進行了模擬研究，模擬結果表明，該方法可極大地提高機械手逆運動學解的精度，確保快速達到全局收斂。
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