陷入法網 的英文怎麼說

中文拼音 [xiànrùfǎwǎng]
陷入法網 英文
be caught in meshes of the law
  • 陷 : Ⅰ名詞1 （陷阱） pitfall; trap2 （缺點） defect; deficiency Ⅱ動詞1 （掉進） get stuck or bogged do...
  • 入 : Ⅰ動詞1 （進來或進去） enter 2 （參加） join; be admitted into; become a member of 3 （合乎） conf...
  • 法 : Ⅰ名詞1 （由國家制定或認可的行為規則的總稱） law 2 （方法; 方式） way; method; mode; means 3 （標...
  • 網 : Ⅰ名詞1 （捕魚捉鳥的器具） net 2 （像網的東西） thing which looks like a net 3 （像網一樣的組織或...
  • 陷入 : 1 （落在不利的境地） sink into; fall into; land oneself in; be caught in; get bogged down in; emb...
  • 法網 : the net of justice; the arm of the law
  1. Thirdly, resorting to cooperation - competition model of biomathematics, this thesis proposes a new co - evolution model. simulation results are shown to verify its effect and practicabilitv. last, standard methods for optimizing neural netvvorks are easily trapped into local optimization, and unable to adjust the structure of neural networks, thus their application is limited to certain extent

    本文主要驗證和設計適應性操作運算元和小生境方法保持群體多樣性的能力，實驗表明兩種方法都能較好地達到目的；利用生物合作競爭模型設計協同演化來動態地改變群體規模，實驗表明該模型是有效的；人工神經網路是得到越來越廣泛應用的學習系統，但是由於學習演算法存在一定的缺陷，如易於陷入局部極值，難以調整網路的結構等，使神經網路的應用受到一定的限制。
  2. People put forward radial basis function networks considering the conventional bp algorithm problems of slow convergence speed and easily getting into local dinky value

    對于傳統bp演算法存在的收斂速度慢和易陷入局部極小值問題，人們提出了徑向基函數網路。
  3. 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。
  4. The thief was caught in the toils of law.

    這個賊陷入了法網。
  5. Instruction detection technology is core in instruction detection system, it include abnormity instruction and abused instruction detection, on the basis of traditional network security model, ppdr model, instruction detection principle and instruction technology analysis, the author has brought forward instruction detection method based genetic neural networks, adopted genetic algometry and bp neural networks union method, and applied in instruction detection system, solve traditional bp algometry lie in absence about constringency rate slowly and immersion minim value

    入侵檢測分析技術是入侵檢測系統的核心，主要分為異常入侵檢測和誤用入侵檢測。作者在對傳統網路安全模型、 ppdr模型、入侵檢測原理以及常用入侵檢測技術進行比較分析的基礎上，提出了一個基於遺傳神經網路的入侵檢測方法，採用遺傳演算法和bp神經網路相結合的方法?遺傳神經網路應用於入侵檢測系統中，解決了傳統的bp演算法的收斂速度慢、易陷入局部最小點的問題。
  6. Compared with the classical bp algorithm, robust adaptive bp algorithm possesses some advantages as following : ( 1 ) increasing the accuracy of the network training by means of using both the relative and absolute residual to adjust the weight values ; ( 2 ) improve the robustness and the network convergence rate through combining with the robust statistic technique by way of judging the values of the samples " relative residual to establish the energy function so that can suppress the effect on network training because of the samples with high noise disturbances ; ( 3 ) prevent entrapping into the local minima area and obtain the global optimal result owing to setting the learning rate to be the function of the errors and the error gradients when network is trained. the learning rate of the weights update change with the error values of the network adaptively so that can easily get rid of the disadvantage of the classical bp algorithm that is liable to entrap into the local minima areas

    與基本bp演算法相比，本文提出的魯棒自適應bp演算法具有以下優點： （ 1 ）與魯棒統計技術相結合，通過訓練樣本相對偏差的大小，確定不同訓練樣本對能量函數的貢獻，來抑制含高噪聲干擾樣本對網路訓練的不良影響，從而增強訓練的魯棒性，提高網路訓練的收斂速度； （ 2 ）採用相對偏差和絕對偏差兩種偏差形式對權值進行調整，提高了網路的訓練精度； （ 3 ）在採用梯度下降演算法對權值進行調整的基礎上，通過將學習速率設為訓練誤差及誤差梯度的特殊函數，使學習速率依賴于網路訓練時誤差瞬時的變化而自適應的改變，從而可以克服基本bp演算法容易陷入局部極小區域的弊端，使訓練過程能夠很快的「跳出」局部極小區域而達到全局最優。
  7. An indirect self - adaptive fuzzy - neural network controller ( fnnc ) has been proposed with its parameters and the structure tuned simultaneously by ga in virtue of the powerful optimization property of ga. the structure of the controller is based on the radical basis function ( rbf ) neural network with gaussian membership functions. the performance of the proposed fnnc is compared with a conventional fuzzy - pid controller and the simulation results show that the fnnc presents encouraging advantages

    針對神經網路採用一維反向傳播訓練演算法速度較慢且易於陷入局部極小點的不足，設計了一種間接自校正模糊神經網路控制系統，利用遺傳演算法（ ca ）對隸屬度函數的結構和參數進行優化，模擬比較表明該控制比模糊pid控制具有更優的性能。
  8. 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。
  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. Creatures in the area in which a web of shadows is forming are allowed a saving throw to get out

    陷入被施展了幽影羅網法術的區域中的生物可以進行一次豁免檢定來決定他是否能夠脫出。
  11. 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。
  12. Normal bp algorithm can be used in many fields and resolved many practical problems, however, normal bp algorithm has many limitations such as it ' s easy to fall into the local minimum in the course of convergence, its " convergent speed is very slow, the method which set the structural parameter and the operational parameter has n ' t be widely accepted, and so on

    標準bp演算法應用甚廣，解決了許多的實際問題，但同時它也存在著諸如在收斂過程中容易陷入局部最小點、收斂速度很慢以及網路的結構參數（隱層數、隱層單元）和運算參數（步長、非線性函數的選擇）等都尚無公認的理論指導等問題。
  13. Many small firms hate google because they relied on exploiting its search formulas to win prime positions in its rankings, but dropped to the internet ' s equivalent of hades after google tweaked these algorithms

    許多小型企業痛恨谷歌，因為他們為了在網站評級中爭得最佳位次而紛紛採用該公司的搜索規則，但這些演算法的調整卻讓他們陷入了網路的深淵。
  14. However, the neural network easily falls into local minimum, and weakly search the overall situation. the genetic algorithm ( ga ) has the ability of searching overall situation. the genetic neural network recombines the genetic algorithm ’ s of seeking the superior overall situation and the neural network ’ s nonlinear characteristic and rapid convergence

    但神經網路具有易陷入局部極小值以及全局搜索能力弱等缺點;而遺傳演算法具有較好的全局最優搜索能力，遺傳神經網路將兩者結合，既保留了遺傳演算法的全局尋優的特點，又兼有神經網路的非線性特性和收斂的快速性。
  15. But it has intrinsic defects such as low convergence and local minimum because the negative gradient method is adopted in weight adjusting. an improved rbf network is introduced which has the advantage in digital approximation, classification and learning rate and at the same time, the corresponding sensibility is also analysed

    但bp網路在用於函數逼近時，權值的調節採用的是負梯度下降法，這種調節方法有它的局限性，如收斂速度慢和容易陷入局部極小等缺點。
  16. In training of back - propagation neural network, parameter adaptable method which can automatically adjust learning rate and inertia factor is employed in order to avoiding systemic error immersed in a local minimum and accelerating the network ' s convergence ; introduced the further optimization of the network ' s structure, it gives the research result of selection of the hidden layers, neurons, and the strategy of re - learning, compared the sums of the deviation square of this algorithm with conventional bp algorithm, as a result, the approach accuracy and the generalization ability of the network were extremely improved

    在對前饋神經網路的訓練中，使用參數自適應方法實現了學習率、慣性因子的自我調節，以避免系統誤差陷入局部最小，加快網路的收斂速度；提出了優化bp網路結構的實驗研究方法，並給出了有關隱含層數和節點數選擇以及再學習策略引進的研究結果。將該演算法同傳統bp演算法的預測偏差平方和進行比較，結果證實網路的逼近精度及泛化能力均得到了極大的提高和改善。
  17. Bp ( back propagation ) algorithm is the most popular training algorithm in applications for its non - linear mapping approach capability and robustness. however, it has some defects, such as converging slowly and immersing in local vibration frequently

    Bp演算法以其良好的非線性映射逼近能力和泛化能力以及易實現性成為人工神經網路應用最廣泛的訓練演算法，但是bp演算法也有其明顯的缺陷，即訓練速度慢、容易陷入局部極值等。
  18. The several ones that have more lager sensitivity to embankment settlement are found out. then, aimed at the traditional three - layer bp network ' s shortages : easily getting into local minimum value and slow convergence, the modification combined momentum method with self - adaptation study velocity is made, and one improved bp network is put forward. finally, according to the results from above sensitivity analyses, the nonlinear model main parameters of each natural layer in roadbed are approximately rectified using the improved bp network technology founded on its stronger nonlinear mapping capacity and the settlement measurements

    採用非線性有限元程序，對鄧肯-張模型中8個參數與路堤沉降的關系進行了詳細分析，找到了影響沉降的主要參數；接著，針對傳統的三層bp網路具有收斂速度慢、易陷入局部極小點等不足，對其進行了修正，提出了改進的bp神經網路模型；最後，根據上述靈敏度分析結果，基於改進的bp網路模型較強的非線性映射能力和前期沉降實測資料，對路基中各天然土層的非線性模型主要參數進行了反分析修正； （ 4 ）路堤沉降計算一維法中考慮應力歷史、側向變形的研究。
  19. Aim at bp arithmetic shortcoming of tend plunging local minimum value, applied amelioration inherit arithmetic in taking object as the approach of searching information to overcome local convergence of nn

    針對bp演算法具有易於陷入局部最小值的缺點，應用改進遺傳演算法直接以目標函數值作為搜索信息途徑，克服神經網路的局部收斂的缺陷。
  20. To improve the velocity of neural network and avoid the minimal value, genetic algorithms as the training algorithm of neural network is adopted

    為了提高網路的收斂速度和避免網路陷入局部極小值，採用遺傳演算法作為該神經網路模型系統的訓練演算法。
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